A Medley of Potpourri: 08/01/2024

Saturday, August 31, 2024

Natural computing

From Wikipedia, the free encyclopedia

Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials (e.g., molecules) to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.

Computational paradigms studied by natural computing are abstracted from natural phenomena as diverse as self-replication, the functioning of the brain, Darwinian evolution, group behavior, the immune system, the defining properties of life forms, cell membranes, and morphogenesis. Besides traditional electronic hardware, these computational paradigms can be implemented on alternative physical media such as biomolecules (DNA, RNA), or trapped-ion quantum computing devices.

Dually, one can view processes occurring in nature as information processing. Such processes include self-assembly, developmental processes, gene regulation networks, protein–protein interaction networks, biological transport (active transport, passive transport) networks, and gene assembly in unicellular organisms. Efforts to understand biological systems also include engineering of semi-synthetic organisms, and understanding the universe itself from the point of view of information processing. Indeed, the idea was even advanced that information is more fundamental than matter or energy. The Zuse-Fredkin thesis, dating back to the 1960s, states that the entire universe is a huge cellular automaton which continuously updates its rules. Recently it has been suggested that the whole universe is a quantum computer that computes its own behaviour. The universe/nature as computational mechanism is addressed by, exploring nature with help the ideas of computability, and studying natural processes as computations (information processing).

Nature-inspired models of computation

The most established "classical" nature-inspired models of computation are cellular automata, neural computation, and evolutionary computation. More recent computational systems abstracted from natural processes include swarm intelligence, artificial immune systems, membrane computing, and amorphous computing. Detailed reviews can be found in many books.

Cellular automata

A cellular automaton is a dynamical system consisting of an array of cells. Space and time are discrete and each of the cells can be in a finite number of states. The cellular automaton updates the states of its cells synchronously according to the transition rules given a priori. The next state of a cell is computed by a transition rule and it depends only on its current state and the states of its neighbors.

Conway's Game of Life is one of the best-known examples of cellular automata, shown to be computationally universal. Cellular automata have been applied to modelling a variety of phenomena such as communication, growth, reproduction, competition, evolution and other physical and biological processes.

Neural computation

Neural computation is the field of research that emerged from the comparison between computing machines and the human nervous system. This field aims both to understand how the brain of living organisms works (brain theory or computational neuroscience), and to design efficient algorithms based on the principles of how the human brain processes information (Artificial Neural Networks, ANN ).

An artificial neural network is a network of artificial neurons. An artificial neuron A is equipped with a function $f_{A}$ , receives n real-valued inputs $x_{1}, x_{2}, \dots, x_{n}$ with respective weights $w_{1}, w_{2}, \dots, w_{n}$ , and it outputs $f_{A} (w_{1} x_{1} + w_{2} x_{2} + \dots + w_{n} x_{n})$ . Some neurons are selected to be the output neurons, and the network function is the vectorial function that associates to the n input values, the outputs of the m selected output neurons. Note that different choices of weights produce different network functions for the same inputs. Back-propagation is a supervised learning method by which the weights of the connections in the network are repeatedly adjusted so as to minimize the difference between the vector of actual outputs and that of desired outputs. Learning algorithms based on backwards propagation of errors can be used to find optimal weights for given topology of the network and input-output pairs.

Evolutionary computation

Evolutionary computation is a computational paradigm inspired by Darwinian evolution.

An artificial evolutionary system is a computational system based on the notion of simulated evolution. It comprises a constant- or variable-size population of individuals, a fitness criterion, and genetically inspired operators that produce the next generation from the current one. The initial population is typically generated randomly or heuristically, and typical operators are mutation and recombination. At each step, the individuals are evaluated according to the given fitness function (survival of the fittest). The next generation is obtained from selected individuals (parents) by using genetically inspired operators. The choice of parents can be guided by a selection operator which reflects the biological principle of mate selection. This process of simulated evolution eventually converges towards a nearly optimal population of individuals, from the point of view of the fitness function.

The study of evolutionary systems has historically evolved along three main branches: Evolution strategies provide a solution to parameter optimization problems for real-valued as well as discrete and mixed types of parameters. Evolutionary programming originally aimed at creating optimal "intelligent agents" modelled, e.g., as finite state machines. Genetic algorithms applied the idea of evolutionary computation to the problem of finding a (nearly-)optimal solution to a given problem. Genetic algorithms initially consisted of an input population of individuals encoded as fixed-length bit strings, the genetic operators mutation (bit flips) and recombination (combination of a prefix of a parent with the suffix of the other), and a problem-dependent fitness function. Genetic algorithms have been used to optimize computer programs, called genetic programming, and today they are also applied to real-valued parameter optimization problems as well as to many types of combinatorial tasks.

Estimation of Distribution Algorithm (EDA), on the other hand, are evolutionary algorithms that substitute traditional reproduction operators by model-guided ones. Such models are learned from the population by employing machine learning techniques and represented as Probabilistic Graphical Models, from which new solutions can be sampled or generated from guided-crossover.

Swarm intelligence

Swarm intelligence, sometimes referred to as collective intelligence, is defined as the problem solving behavior that emerges from the interaction of individual agents (e.g., bacteria, ants, termites, bees, spiders, fish, birds) which communicate with other agents by acting on their local environments.

Particle swarm optimization applies this idea to the problem of finding an optimal solution to a given problem by a search through a (multi-dimensional) solution space. The initial set-up is a swarm of particles, each representing a possible solution to the problem. Each particle has its own velocity which depends on its previous velocity (the inertia component), the tendency towards the past personal best position (the nostalgia component), and its tendency towards a global neighborhood optimum or local neighborhood optimum (the social component). Particles thus move through a multidimensional space and eventually converge towards a point between the global best and their personal best. Particle swarm optimization algorithms have been applied to various optimization problems, and to unsupervised learning, game learning, and scheduling applications.

In the same vein, ant algorithms model the foraging behaviour of ant colonies. To find the best path between the nest and a source of food, ants rely on indirect communication by laying a pheromone trail on the way back to the nest if they found food, respectively following the concentration of pheromones if they are looking for food. Ant algorithms have been successfully applied to a variety of combinatorial optimization problems over discrete search spaces.

Artificial immune systems

Artificial immune systems (a.k.a. immunological computation or immunocomputing) are computational systems inspired by the natural immune systems of biological organisms.

Viewed as an information processing system, the natural immune system of organisms performs many complex tasks in parallel and distributed computing fashion. These include distinguishing between self and nonself, neutralization of nonself pathogens (viruses, bacteria, fungi, and parasites), learning, memory, associative retrieval, self-regulation, and fault-tolerance. Artificial immune systems are abstractions of the natural immune system, emphasizing these computational aspects. Their applications include computer virus detection, anomaly detection in a time series of data, fault diagnosis, pattern recognition, machine learning, bioinformatics, optimization, robotics and control.

Membrane computing

Membrane computing investigates computing models abstracted from the compartmentalized structure of living cells affected by membranes. A generic membrane system (P-system) consists of cell-like compartments (regions) delimited by membranes, that are placed in a nested hierarchical structure. Each membrane-enveloped region contains objects, transformation rules which modify these objects, as well as transfer rules, which specify whether the objects will be transferred outside or stay inside the region. Regions communicate with each other via the transfer of objects. The computation by a membrane system starts with an initial configuration, where the number (multiplicity) of each object is set to some value for each region (multiset of objects). It proceeds by choosing, nondeterministically and in a maximally parallel manner, which rules are applied to which objects. The output of the computation is collected from an a priori determined output region.

Applications of membrane systems include machine learning, modelling of biological processes (photosynthesis, certain signaling pathways, quorum sensing in bacteria, cell-mediated immunity), as well as computer science applications such as computer graphics, public-key cryptography, approximation and sorting algorithms, as well as analysis of various computationally hard problems.

Amorphous computing

In biological organisms, morphogenesis (the development of well-defined shapes and functional structures) is achieved by the interactions between cells guided by the genetic program encoded in the organism's DNA.

Inspired by this idea, amorphous computing aims at engineering well-defined shapes and patterns, or coherent computational behaviours, from the local interactions of a multitude of simple unreliable, irregularly placed, asynchronous, identically programmed computing elements (particles). As a programming paradigm, the aim is to find new programming techniques that would work well for amorphous computing environments. Amorphous computing also plays an important role as the basis for "cellular computing" (see the topics synthetic biology and cellular computing, below).

Morphological computing

The understanding that the morphology performs computation is used to analyze the relationship between morphology and control and to theoretically guide the design of robots with reduced control requirements, has been used in both robotics and for understanding of cognitive processes in living organisms, see Morphological computation and ...

Cognitive computing

Cognitive computing CC is a new type of computing, typically with the goal of modelling of functions of human sensing, reasoning, and response to stimulus, see Cognitive computing and .

Cognitive capacities of present-day cognitive computing are far from human level. The same info-computational approach can be applied to other, simpler living organisms. Bacteria are an example of a cognitive system modelled computationally, see Eshel Ben-Jacob and Microbes-mind.

Synthesizing nature by means of computing

Artificial life

Artificial life (ALife) is a research field whose ultimate goal is to understand the essential properties of life organisms by building, within electronic computers or other artificial media, ab initio systems that exhibit properties normally associated only with living organisms. Early examples include Lindenmayer systems (L-systems), that have been used to model plant growth and development. An L-system is a parallel rewriting system that starts with an initial word, and applies its rewriting rules in parallel to all letters of the word.

Pioneering experiments in artificial life included the design of evolving "virtual block creatures" acting in simulated environments with realistic features such as kinetics, dynamics, gravity, collision, and friction. These artificial creatures were selected for their abilities endowed to swim, or walk, or jump, and they competed for a common limited resource (controlling a cube). The simulation resulted in the evolution of creatures exhibiting surprising behaviour: some developed hands to grab the cube, others developed legs to move towards the cube. This computational approach was further combined with rapid manufacturing technology to actually build the physical robots that virtually evolved. This marked the emergence of the field of mechanical artificial life.

The field of synthetic biology explores a biological implementation of similar ideas. Other research directions within the field of artificial life include artificial chemistry as well as traditionally biological phenomena explored in artificial systems, ranging from computational processes such as co-evolutionary adaptation and development, to physical processes such as growth, self-replication, and self-repair.

Nature-inspired novel hardware

All of the computational techniques mentioned above, while inspired by nature, have been implemented until now mostly on traditional electronic hardware. In contrast, the two paradigms introduced here, molecular computing and quantum computing, employ radically different types of hardware.

Molecular computing

Molecular computing (a.k.a. biomolecular computing, biocomputing, biochemical computing, DNA computing) is a computational paradigm in which data is encoded as biomolecules such as DNA strands, and molecular biology tools act on the data to perform various operations (e.g., arithmetic or logical operations).

The first experimental realization of special-purpose molecular computer was the 1994 breakthrough experiment by Leonard Adleman who solved a 7-node instance of the Hamiltonian Path Problem solely by manipulating DNA strands in test tubes. DNA computations start from an initial input encoded as a DNA sequence (essentially a sequence over the four-letter alphabet {A, C, G, T}), and proceed by a succession of bio-operations such as cut-and-paste (by restriction enzymes and ligases), extraction of strands containing a certain subsequence (by using Watson-Crick complementarity), copy (by using polymerase chain reaction that employs the polymerase enzyme), and read-out. Recent experimental research succeeded in solving more complex instances of NP-complete problems such as a 20-variable instance of 3SAT, and wet DNA implementations of finite state machines with potential applications to the design of smart drugs.

One of the most notable contributions of research in this field is to the understanding of self-assembly. Self-assembly is the bottom-up process by which objects autonomously come together to form complex structures. Instances in nature abound, and include atoms binding by chemical bonds to form molecules, and molecules forming crystals or macromolecules. Examples of self-assembly research topics include self-assembled DNA nanostructures such as Sierpinski triangles or arbitrary nanoshapes obtained using the DNA origami technique, and DNA nanomachines such as DNA-based circuits (binary counter, bit-wise cumulative XOR), ribozymes for logic operations, molecular switches (DNA tweezers), and autonomous molecular motors (DNA walkers).

Theoretical research in molecular computing has yielded several novel models of DNA computing (e.g. splicing systems introduced by Tom Head already in 1987) and their computational power has been investigated. Various subsets of bio-operations are now known to be able to achieve the computational power of Turing machines.

Quantum computing

A quantum computer processes data stored as quantum bits (qubits), and uses quantum mechanical phenomena such as superposition and entanglement to perform computations. A qubit can hold a "0", a "1", or a quantum superposition of these. A quantum computer operates on qubits with quantum logic gates. Through Shor's polynomial algorithm for factoring integers, and Grover's algorithm for quantum database search that has a quadratic time advantage, quantum computers were shown to potentially possess a significant benefit relative to electronic computers.

Quantum cryptography is not based on the complexity of the computation, but on the special properties of quantum information, such as the fact that quantum information cannot be measured reliably and any attempt at measuring it results in an unavoidable and irreversible disturbance. A successful open air experiment in quantum cryptography was reported in 2007, where data was transmitted securely over a distance of 144 km. Quantum teleportation is another promising application, in which a quantum state (not matter or energy) is transferred to an arbitrary distant location. Implementations of practical quantum computers are based on various substrates such as ion-traps, superconductors, nuclear magnetic resonance, etc. As of 2006, the largest quantum computing experiment used liquid state nuclear magnetic resonance quantum information processors, and could operate on up to 12 qubits.

Nature as information processing

The dual aspect of natural computation is that it aims to understand nature by regarding natural phenomena as information processing. Already in the 1960s, Zuse and Fredkin suggested the idea that the entire universe is a computational (information processing) mechanism, modelled as a cellular automaton which continuously updates its rules. A recent quantum-mechanical approach of Lloyd suggests the universe as a quantum computer that computes its own behaviour, while Vedral suggests that information is the most fundamental building block of reality.

The universe/nature as computational mechanism is elaborated in, exploring the nature with help of the ideas of computability, whilst, based on the idea of nature as network of networks of information processes on different levels of organization, is studying natural processes as computations (information processing).

The main directions of research in this area are systems biology, synthetic biology and cellular computing.

Systems biology

Computational systems biology (or simply systems biology) is an integrative and qualitative approach that investigates the complex communications and interactions taking place in biological systems. Thus, in systems biology, the focus of the study is the interaction networks themselves and the properties of biological systems that arise due to these networks, rather than the individual components of functional processes in an organism. This type of research on organic components has focused strongly on four different interdependent interaction networks: gene-regulatory networks, biochemical networks, transport networks, and carbohydrate networks.

Gene regulatory networks comprise gene-gene interactions, as well as interactions between genes and other substances in the cell. Genes are transcribed into messenger RNA (mRNA), and then translated into proteins according to the genetic code. Each gene is associated with other DNA segments (promoters, enhancers, or silencers) that act as binding sites for activators or repressors for gene transcription. Genes interact with each other either through their gene products (mRNA, proteins) which can regulate gene transcription, or through small RNA species that can directly regulate genes. These gene-gene interactions, together with genes' interactions with other substances in the cell, form the most basic interaction network: the gene regulatory networks. They perform information processing tasks within the cell, including the assembly and maintenance of other networks. Models of gene regulatory networks include random and probabilistic Boolean networks, asynchronous automata, and network motifs.

Another viewpoint is that the entire genomic regulatory system is a computational system, a genomic computer. This interpretation allows one to compare human-made electronic computation with computation as it occurs in nature.

A comparison between genomic and electronic computers
	Genomic computer	Electronic computer
Architecture	changeable	rigid
Components construction	as-needed basis	from the start
Coordination	causal coordination	temporal synchrony
Distinction between hardware and software	No	Yes
Transport media	molecules and ions	wires

In addition, unlike a conventional computer, robustness in a genomic computer is achieved by various feedback mechanisms by which poorly functional processes are rapidly degraded, poorly functional cells are killed by apoptosis, and poorly functional organisms are out-competed by more fit species.

Biochemical networks refer to the interactions between proteins, and they perform various mechanical and metabolic tasks inside a cell. Two or more proteins may bind to each other via binding of their interactions sites, and form a dynamic protein complex (complexation). These protein complexes may act as catalysts for other chemical reactions, or may chemically modify each other. Such modifications cause changes to available binding sites of proteins. There are tens of thousands of proteins in a cell, and they interact with each other. To describe such a massive scale interactions, Kohn maps were introduced as a graphical notation to depict molecular interactions in succinct pictures. Other approaches to describing accurately and succinctly protein–protein interactions include the use of textual bio-calculus or pi-calculus enriched with stochastic features.

Transport networks refer to the separation and transport of substances mediated by lipid membranes. Some lipids can self-assemble into biological membranes. A lipid membrane consists of a lipid bilayer in which proteins and other molecules are embedded, being able to travel along this layer. Through lipid bilayers, substances are transported between the inside and outside of membranes to interact with other molecules. Formalisms depicting transport networks include membrane systems and brane calculi.

Synthetic biology

Synthetic biology aims at engineering synthetic biological components, with the ultimate goal of assembling whole biological systems from their constituent components. The history of synthetic biology can be traced back to the 1960s, when François Jacob and Jacques Monod discovered the mathematical logic in gene regulation. Genetic engineering techniques, based on recombinant DNA technology, are a precursor of today's synthetic biology which extends these techniques to entire systems of genes and gene products.

Along with the possibility of synthesizing longer and longer DNA strands, the prospect of creating synthetic genomes with the purpose of building entirely artificial synthetic organisms became a reality. Indeed, rapid assembly of chemically synthesized short DNA strands made it possible to generate a 5386bp synthetic genome of a virus.

Alternatively, Smith et al. found about 100 genes that can be removed individually from the genome of Mycoplasma Genitalium. This discovery paves the way to the assembly of a minimal but still viable artificial genome consisting of the essential genes only.

A third approach to engineering semi-synthetic cells is the construction of a single type of RNA-like molecule with the ability of self-replication. Such a molecule could be obtained by guiding the rapid evolution of an initial population of RNA-like molecules, by selection for the desired traits.

Another effort in this field is towards engineering multi-cellular systems by designing, e.g., cell-to-cell communication modules used to coordinate living bacterial cell populations.

Cellular computing

Computation in living cells (a.k.a. cellular computing, or in-vivo computing) is another approach to understand nature as computation. One particular study in this area is that of the computational nature of gene assembly in unicellular organisms called ciliates. Ciliates store a copy of their DNA containing functional genes in the macronucleus, and another "encrypted" copy in the micronucleus. Conjugation of two ciliates consists of the exchange of their micronuclear genetic information, leading to the formation of two new micronuclei, followed by each ciliate re-assembling the information from its new micronucleus to construct a new functional macronucleus. The latter process is called gene assembly, or gene re-arrangement. It involves re-ordering some fragments of DNA (permutations and possibly inversion) and deleting other fragments from the micronuclear copy. From the computational point of view, the study of this gene assembly process led to many challenging research themes and results, such as the Turing universality of various models of this process. From the biological point of view, a plausible hypothesis about the "bioware" that implements the gene-assembly process was proposed, based on template guided recombination.

Other approaches to cellular computing include developing an in vivo programmable and autonomous finite-state automaton with E. coli, designing and constructing in vivo cellular logic gates and genetic circuits that harness the cell's existing biochemical processes and the global optimization of stomata aperture in leaves, following a set of local rules resembling a cellular automaton.

Antibody–drug conjugate

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Antibody%E2%80%93drug_conjugate

Antibody–drug conjugates or ADCs are a class of biopharmaceutical drugs designed as a targeted therapy for treating cancer. Unlike chemotherapy, ADCs are intended to target and kill tumor cells while sparing healthy cells. As of 2019, some 56 pharmaceutical companies were developing ADCs.

ADCs are complex molecules composed of an antibody linked to a biologically active cytotoxic (anticancer) payload or drug. Antibody–drug conjugates are an example of bioconjugates and immunoconjugates.

ADCs combine the targeting properties of monoclonal antibodies with the cancer-killing capabilities of cytotoxic drugs, designed to discriminate between healthy and diseased tissue.

Mechanism of action

An anticancer drug is coupled to an antibody that targets a specific tumor antigen (or protein) that, ideally, is only found in or on tumor cells. Antibodies attach themselves to the antigens on the surface of cancerous cells. The biochemical reaction that occurs upon attaching triggers a signal in the tumor cell, which then absorbs, or internalizes, the antibody together with the linked cytotoxin. After the ADC is internalized, the cytotoxin kills the cancer. Their targeting ability was believed to limit side effects for cancer patients and to give a wider therapeutic window than other chemotherapeutic agents, although this promise hasn't yet been realized in the clinic.

ADC technologies have been featured in many publications, including scientific journals.

History

The idea of drugs that would target tumor cells and ignore others was conceived in 1900 by German Nobel laureate Paul Ehrlich; he described the drugs as a "magic bullet" due to their targeting properties.

In 2001 Pfizer/Wyeth's drug Gemtuzumab ozogamicin (trade name: Mylotarg) was approved based on a study with a surrogate endpoint, through the accelerated approval process. In June 2010, after evidence accumulated showing no evidence of benefit and significant toxicity, the U.S. Food and Drug Administration (FDA) forced the company to withdraw it. It was reintroduced into the US market in 2017.

Brentuximab vedotin (trade name: Adcetris, marketed by Seattle Genetics and Millennium/Takeda) was approved for relapsed HL and relapsed systemic anaplastic large-cell lymphoma (sALCL)) by the FDA on August 19, 2011 and received conditional marketing authorization from the European Medicines Agency in October 2012.

Trastuzumab emtansine (ado-trastuzumab emtansine or T-DM1, trade name: Kadcyla, marketed by Genentech and Roche) was approved in February 2013 for the treatment of people with HER2-positive metastatic breast cancer (mBC) who had received prior treatment with trastuzumab and a taxane chemotherapy.

The European Commission approved Inotuzumab ozogamicin as a monotherapy for the treatment of adults with relapsed or refractory CD22-positive B-cell precursor acute lymphoblastic leukemia (ALL) on June 30, 2017 under the trade name Besponsa® (Pfizer/Wyeth), followed on August 17, 2017 by the FDA.

The first immunology antibody–drug conjugate (iADC), ABBV-3373, showed an improvement in disease activity in a Phase 2a study of patients with rheumatoid arthritis and a study with the second iADC, ABBV-154 to evaluate adverse events and change in disease activity in participants treated with subcutaneous injection of ABBV-154 is ongoing.

In July 2018, Daiichi Sankyo Company, Limited and Glycotope GmbH have inked a pact regarding the combination of Glycotope's investigational tumor-associated TA-MUC1 antibody gatipotuzumab and Daiichi Sankyo's proprietary ADC technology for developing gatipotuzumab antibody drug conjugate.

In 2019 AstraZeneca agreed to pay up to US$6.9 billion to jointly develop DS-8201 with Japan's Daiichi Sankyo. It is intended to replace Herceptin for treating breast cancer. DS8201 carries eight payloads, compared to the usual four.

Commercial products

Thirteen ADCs have received market approval by the FDA – all for oncotherapies. Belantamab mafodotin is in the process of being withdrawn from US marketing.

FDA Approved ADCs
Drug	Trade name	Maker	Condition
Gemtuzumab ozogamicin	Mylotarg	Pfizer/Wyeth	relapsed acute myelogenous leukemia (AML)
Brentuximab vedotin	Adcetris	Seattle Genetics, Millennium/Takeda	Hodgkin lymphoma (HL) and systemic anaplastic large-cell lymphoma (ALCL)
Trastuzumab emtansine	Kadcyla	Genentech, Roche	HER2-positive metastatic breast cancer (mBC) following treatment with trastuzumab and a maytansinoid
Inotuzumab ozogamicin	Besponsa	Pfizer/Wyeth	relapsed or refractory CD22-positive B-cell precursor acute lymphoblastic leukemia
Polatuzumab vedotin	Polivy	Genentech, Roche	relapsed or refractory diffuse large B-cell lymphoma (DLBCL)
Enfortumab vedotin	Padcev	Astellas/Seattle Genetics	adult patients with locally advanced or metastatic urothelial cancer who have received a PD-1 or PD-L1 inhibitor, and a Pt-containing therapy
Trastuzumab deruxtecan	Enhertu	AstraZeneca/Daiichi Sankyo	adult patients with unresectable or metastatic HER2-positive breast cancer who have received two or more prior anti-HER2 based regimens
Sacituzumab govitecan	Trodelvy	Immunomedics	adult patients with metastatic triple-negative breast cancer (mTNBC) who have received at least two prior therapies for patients with relapsed or refractory metastatic disease
Belantamab mafodotin	Blenrep	GlaxoSmithKline	multiple myeloma patients whose disease has progressed despite prior treatment with an immunomodulatory agent, proteasome inhibitor and anti-CD38 antibody
Moxetumomab pasudotox	Lumoxiti	AstraZeneca	relapsed or refractory hairy cell leukemia (HCL)
Loncastuximab tesirine	Zynlonta	ADC Therapeutics	relapsed or refractory large B-cell lymphoma (including diffuse large B-cell lymphoma (DLBCL) not otherwise specified, DLBCL arising from low-grade lymphoma, and high-grade B-cell lymphoma) after two or more lines of systemic therapy
Tisotumab vedotin-tftv	Tivdak	Seagen Inc, Genmab	adult patients with recurrent or metastatic cervical cancer with disease progression on or after chemotherapy
Mirvetuximab soravtansine	Elahere	ImmunoGen	treatment of adult patients with folate receptor alpha (FRα)-positive, platinum-resistant epithelial ovarian, fallopian tube, or primary peritoneal cancer, who have received one to three prior systemic treatment regimens

Components of an ADC

An antibody–drug conjugate consists of 3 components:

Antibody - targets the cancer cell surface and may also elicit a therapeutic response.
Payload - elicits the desired therapeutic response.
Linker - attaches the payload to the antibody and should be stable in circulation only releasing the payload at the desired target. Multiple approaches to conjugation have been developed for attachment to the antibody and reviewed. DAR is the drug to antibody ratio and indicates the level of loading of the payload on the ADC.

Payloads

Many of the payloads for oncology ADCs (oADC) are natural product based with some making covalent interactions with their target. Payloads include the microtubulin inhibitors monomethyl auristatin E (MMAE), monomethyl auristatin F (MMAF) and mertansine, DNA binder calicheamicin and topoisomerase 1 inhibitors SN-38 and exatecan resulting in a renaissance for natural product total synthesis. Glucocorticoid receptor modulators (GRMs) represent to most active payload class for iADCs. Approaches releasing marketed GRM molecules such as dexamethasone and budesonide have been developed. Modified GRM molecules have also been developed that enable the attachment of the linker with the term ADCidified describing the medicinal chemistry process of payload optimization to facilitate linker attachment. Alternatives to small molecule payloads have also been investigated, for example, siRNA.

Linkers

A stable link between the antibody and cytotoxic (anti-cancer) agent is a crucial aspect of an ADC. A stable ADC linker ensures that less of the cytotoxic payload falls off before reaching a tumor cell, improving safety, and limiting dosages.

Linkers are based on chemical motifs including disulfides, hydrazones or peptides (cleavable), or thioethers (noncleavable). Cleavable and noncleavable linkers were proved to be safe in preclinical and clinical trials. Brentuximab vedotin includes an enzyme-sensitive cleavable linker that delivers the antimicrotubule agent monomethyl auristatin E or MMAE, a synthetic antineoplastic agent, to human-specific CD30-positive malignant cells. MMAE inhibits cell division by blocking the polymerization of tubulin. Because of its high toxicity MMAE cannot be used as a single-agent chemotherapeutic drug. However, MMAE linked to an anti-CD30 monoclonal antibody (cAC10, a cell membrane protein of the tumor necrosis factor or TNF receptor) was stable in extracellular fluid. It is cleavable by cathepsin and safe for therapy. Trastuzumab emtansine is a combination of the microtubule-formation inhibitor mertansine (DM-1) and antibody trastuzumab that employs a stable, non-cleavable linker.

The availability of better and more stable linkers has changed the function of the chemical bond. The type of linker, cleavable or noncleavable, lends specific properties to the cytotoxic drug. For example, a non-cleavable linker keeps the drug within the cell. As a result, the entire antibody, linker and cytotoxic (anti-cancer) agent enter the targeted cancer cell where the antibody is degraded into an amino acid. The resulting complex – amino acid, linker and cytotoxic agent – is considered to be the active drug. In contrast, cleavable linkers are detached by enzymes in the cancer cell. The cytotoxic payload can then escape from the targeted cell and, in a process called "bystander killing", attack neighboring cells.

Another type of cleavable linker, currently in development, adds an extra molecule between the cytotoxin and the cleavage site. This allows researchers to create ADCs with more flexibility without changing cleavage kinetics. Researchers are developing a new method of peptide cleavage based on Edman degradation, a method of sequencing amino acids in a peptide. Also under development are site-specific conjugation (TDCs) and novel conjugation techniques to further improve stability and therapeutic index, α emitting immunoconjugates, antibody-conjugated nanoparticles and antibody-oligonucleotide conjugates.

Anything Drug Conjugates

As the antibody–drug conjugate field has matured, a more accurate definition of ADC is now Anything-Drug Conjugate. Alternatives for the antibody targeting component now include multiple smaller antibody fragments like diabodies, Fab, scFv, and bicyclic peptides.

Research

Non-natural amino acids

The first generation uses linking technologies that conjugate drugs non-selectively to cysteine or lysine residues in the antibody, resulting in a heterogeneous mixture. This approach leads to suboptimal safety and efficacy and complicates optimization of the biological, physical and pharmacological properties. Site-specific incorporation of unnatural amino acids generates a site for controlled and stable attachment. This enables the production of homogeneous ADCs with the antibody precisely linked to the drug and controlled ratios of antibody to drug, allowing the selection of a best-in-class ADC. An Escherichia coli-based open cell-free synthesis (OCFS) allows the synthesis of proteins containing site-specifically incorporated non-natural amino acids and has been optimized for predictable high-yield protein synthesis and folding. The absence of a cell wall allows the addition of non-natural factors to the system to manipulate transcription, translation and folding to provide precise protein expression modulation.

Other disease areas

The majority of ADCs under development or in clinical trials are for oncological and hematological indications. This is primarily driven by the inventory of monoclonal antibodies, which target various types of cancer. However, some developers are looking to expand the application to other important disease areas.

Anthropogenic metabolism

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Anthropogenic_metabolism

Anthropogenic metabolism, also referred to as metabolism of the anthroposphere, is a term used in industrial ecology, material flow analysis, and waste management to describe the material and energy turnover of human society. It emerges from the application of systems thinking to the industrial and other man-made activities and it is a central concept of sustainable development. In modern societies, the bulk of anthropogenic (man-made) material flows is related to one of the following activities: sanitation, transportation, habitation, and communication, which were "of little metabolic significance in prehistoric times". Global man-made stocks of steel in buildings, infrastructure, and vehicles, for example, amount to about 25 Gigatonnes (more than three tonnes per person), a figure that is surpassed only by construction materials such as concrete. Sustainable development is closely linked to the design of a sustainable anthropogenic metabolism, which will entail substantial changes in the energy and material turnover of the different human activities. Anthropogenic metabolism can be seen as synonymous to social or socioeconomic metabolism. It comprises both industrial metabolism and urban metabolism.

Negative effects

In layman's terms, anthropogenic metabolism indicates the human impact on the world by the modern industrialized world. Much of these impacts include waste management, ecological footprints, water footprints, and flow analysis (i.e., the rate at which each human depleted the energy around them). Most anthropogenic metabolism happens in developed countries. According to Rosales, "Economic growth is at present the main cause of increased climate change, and climate change is a main mechanism of biodiversity loss; because of this, economic growth is a major catalyst of biodiversity loss."

A water footprint is the amount of water that each person uses in their daily lives. Most of the world's water is salt water which cannot be used in human food or water supplies. Therefore, the freshwater sources that were once plentiful are now being diminished due to anthropogenic metabolism of the growing population. The water footprint encompasses how much fresh water is needed for each consumer's needs. According to J. Allan, "there is a huge impact of water use on stores of surface and groundwater and on flows to which water is returned after use. These impacts are shown to be particularly high for manufacturing industries. For example, that there are less than 10 economies worldwide that have a significant water surplus, but that these economies have successfully met, or have the potential to meet, the water deficits of the other 190 economies. Consumers enjoy the delusion of food and water security provided by virtual water trade.

In addition, the ecological footprint is a more economical and land-focused way of looking at human impact. Developed countries tend to have higher ecological footprints, which do not strictly correspond to a country's total population. According to research by Dias de Oliveira, Vaughan and Rykiel, "The Ecological Footprint...is an accounting tool based on two fundamental concepts, sustainability and carrying capacity. It makes it possible to estimate the resource consumption and waste assimilation requirements of a defined human population or economy sector in terms of corresponding productive land area."

One of the major cycles that humans can contribute to that cause a major impact on climate change is the nitrogen cycle. This comes from nitrogen fertilizers that humans use. Gruber and Galloway have researched, "The massive acceleration of the nitrogen cycle caused by the production and industrial use of artificial nitrogen fertilizers worldwide has led to a range of environmental problems. Most important is how the availability of nitrogen will affect the capacity of Earth's biosphere to continue absorbing carbon from the atmosphere and to thereby continue helping to mitigate climate change."

The carbon cycle is another major contributor to climate change primarily from anthropogenic metabolism. A couple examples of how humans contribute to the carbon in the atmosphere is by burning fossil fuels and deforestation. By taking a close look at the carbon cycle Peng, Thomas and Tian have discovered that, "It is recognized that human activities, such as fossil fuel burning, land-use change, and forest harvesting at a large scale, have resulted in the increase of greenhouse gases in the atmosphere since the onset of the Industrial Revolution. The increasing amounts of greenhouse gases, particularly CO₂ in the atmosphere, is believed to have induced climate change and global warming."

Impact of climate change extend beyond humans. There is a forecast for extinctions of species because of their habitats being affected. An example of this is marine animals. There are major impacts on the marine systems as a result of anthropogenic metabolism, according to Blaustein, the dramatic findings indicate that "every square kilometer [is] affected by some anthropogenic driver of ecological change".

The negative effects of anthropogenic metabolism are seen through the water footprint, ecological footprint, carbon cycle, and the nitrogen cycle. Studies on the marine ecosystem that show major impacts by humans and developed countries which include more industries, thus more anthropogenic metabolism.

Metabolism

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Metabolism

Simplified view of the cellular metabolism

Structure of adenosine triphosphate (ATP), a central intermediate in energy metabolism

Metabolism (/məˈtæbəlɪzəm/, from Greek: μεταβολή metabolē, "change") is the set of life-sustaining chemical reactions in organisms. The three main functions of metabolism are: the conversion of the energy in food to energy available to run cellular processes; the conversion of food to building blocks of proteins, lipids, nucleic acids, and some carbohydrates; and the elimination of metabolic wastes. These enzyme-catalyzed reactions allow organisms to grow and reproduce, maintain their structures, and respond to their environments. The word metabolism can also refer to the sum of all chemical reactions that occur in living organisms, including digestion and the transportation of substances into and between different cells, in which case the above described set of reactions within the cells is called intermediary (or intermediate) metabolism.

Metabolic reactions may be categorized as catabolic—the breaking down of compounds (for example, of glucose to pyruvate by cellular respiration); or anabolic—the building up (synthesis) of compounds (such as proteins, carbohydrates, lipids, and nucleic acids). Usually, catabolism releases energy, and anabolism consumes energy.

The chemical reactions of metabolism are organized into metabolic pathways, in which one chemical is transformed through a series of steps into another chemical, each step being facilitated by a specific enzyme. Enzymes are crucial to metabolism because they allow organisms to drive desirable reactions that require energy and will not occur by themselves, by coupling them to spontaneous reactions that release energy. Enzymes act as catalysts—they allow a reaction to proceed more rapidly—and they also allow the regulation of the rate of a metabolic reaction, for example in response to changes in the cell's environment or to signals from other cells.

The metabolic system of a particular organism determines which substances it will find nutritious and which poisonous. For example, some prokaryotes use hydrogen sulfide as a nutrient, yet this gas is poisonous to animals. The basal metabolic rate of an organism is the measure of the amount of energy consumed by all of these chemical reactions.

A striking feature of metabolism is the similarity of the basic metabolic pathways among vastly different species. For example, the set of carboxylic acids that are best known as the intermediates in the citric acid cycle are present in all known organisms, being found in species as diverse as the unicellular bacterium Escherichia coli and huge multicellular organisms like elephants. These similarities in metabolic pathways are likely due to their early appearance in evolutionary history, and their retention is likely due to their efficacy. In various diseases, such as type II diabetes, metabolic syndrome, and cancer, normal metabolism is disrupted. The metabolism of cancer cells is also different from the metabolism of normal cells, and these differences can be used to find targets for therapeutic intervention in cancer.

Key biochemicals

Most of the structures that make up animals, plants and microbes are made from four basic classes of molecules: amino acids, carbohydrates, nucleic acid and lipids (often called fats). As these molecules are vital for life, metabolic reactions either focus on making these molecules during the construction of cells and tissues, or on breaking them down and using them to obtain energy, by their digestion. These biochemicals can be joined to make polymers such as DNA and proteins, essential macromolecules of life.

Type of molecule	Name of monomer forms	Name of polymer forms	Examples of polymer forms
Amino acids	Amino acids	Proteins (made of polypeptides)	Fibrous proteins and globular proteins
Carbohydrates	Monosaccharides	Polysaccharides	Starch, glycogen and cellulose
Nucleic acids	Nucleotides	Polynucleotides	DNA and RNA

Amino acids and proteins

Proteins are made of amino acids arranged in a linear chain joined by peptide bonds. Many proteins are enzymes that catalyze the chemical reactions in metabolism. Other proteins have structural or mechanical functions, such as those that form the cytoskeleton, a system of scaffolding that maintains the cell shape. Proteins are also important in cell signaling, immune responses, cell adhesion, active transport across membranes, and the cell cycle. Amino acids also contribute to cellular energy metabolism by providing a carbon source for entry into the citric acid cycle (tricarboxylic acid cycle), especially when a primary source of energy, such as glucose, is scarce, or when cells undergo metabolic stress.

Lipids

Lipids are the most diverse group of biochemicals. Their main structural uses are as part of internal and external biological membranes, such as the cell membrane. Their chemical energy can also be used. Lipids contain a long, non-polar hydrocarbon chain with a small polar region containing oxygen. Lipids are usually defined as hydrophobic or amphipathic biological molecules but will dissolve in organic solvents such as ethanol, benzene or chloroform. The fats are a large group of compounds that contain fatty acids and glycerol; a glycerol molecule attached to three fatty acids by ester linkages is called a triacylglyceride. Several variations of the basic structure exist, including backbones such as sphingosine in sphingomyelin, and hydrophilic groups such as phosphate in phospholipids. Steroids such as sterol are another major class of lipids.

Carbohydrates

The straight chain form consists of four C H O H groups linked in a row, capped at the ends by an aldehyde group C O H and a methanol group C H 2 O H. To form the ring, the aldehyde group combines with the O H group of the next-to-last carbon at the other end, just before the methanol group. — Glucose can exist in both a straight-chain and ring form.

Carbohydrates are aldehydes or ketones, with many hydroxyl groups attached, that can exist as straight chains or rings. Carbohydrates are the most abundant biological molecules, and fill numerous roles, such as the storage and transport of energy (starch, glycogen) and structural components (cellulose in plants, chitin in animals). The basic carbohydrate units are called monosaccharides and include galactose, fructose, and most importantly glucose. Monosaccharides can be linked together to form polysaccharides in almost limitless ways.

Nucleotides

The two nucleic acids, DNA and RNA, are polymers of nucleotides. Each nucleotide is composed of a phosphate attached to a ribose or deoxyribose sugar group which is attached to a nitrogenous base. Nucleic acids are critical for the storage and use of genetic information, and its interpretation through the processes of transcription and protein biosynthesis. This information is protected by DNA repair mechanisms and propagated through DNA replication. Many viruses have an RNA genome, such as HIV, which uses reverse transcription to create a DNA template from its viral RNA genome. RNA in ribozymes such as spliceosomes and ribosomes is similar to enzymes as it can catalyze chemical reactions. Individual nucleosides are made by attaching a nucleobase to a ribose sugar. These bases are heterocyclic rings containing nitrogen, classified as purines or pyrimidines. Nucleotides also act as coenzymes in metabolic-group-transfer reactions.

Coenzymes

Structure of the coenzyme acetyl-CoA. The transferable acetyl group is bonded to the sulfur atom at the extreme left.

Metabolism involves a vast array of chemical reactions, but most fall under a few basic types of reactions that involve the transfer of functional groups of atoms and their bonds within molecules. This common chemistry allows cells to use a small set of metabolic intermediates to carry chemical groups between different reactions. These group-transfer intermediates are called coenzymes. Each class of group-transfer reactions is carried out by a particular coenzyme, which is the substrate for a set of enzymes that produce it, and a set of enzymes that consume it. These coenzymes are therefore continuously made, consumed and then recycled.

One central coenzyme is adenosine triphosphate (ATP), the energy currency of cells. This nucleotide is used to transfer chemical energy between different chemical reactions. There is only a small amount of ATP in cells, but as it is continuously regenerated, the human body can use about its own weight in ATP per day. ATP acts as a bridge between catabolism and anabolism. Catabolism breaks down molecules, and anabolism puts them together. Catabolic reactions generate ATP, and anabolic reactions consume it. It also serves as a carrier of phosphate groups in phosphorylation reactions.

A vitamin is an organic compound needed in small quantities that cannot be made in cells. In human nutrition, most vitamins function as coenzymes after modification; for example, all water-soluble vitamins are phosphorylated or are coupled to nucleotides when they are used in cells. Nicotinamide adenine dinucleotide (NAD⁺), a derivative of vitamin B₃ (niacin), is an important coenzyme that acts as a hydrogen acceptor. Hundreds of separate types of dehydrogenases remove electrons from their substrates and reduce NAD⁺ into NADH. This reduced form of the coenzyme is then a substrate for any of the reductases in the cell that need to transfer hydrogen atoms to their substrates. Nicotinamide adenine dinucleotide exists in two related forms in the cell, NADH and NADPH. The NAD⁺/NADH form is more important in catabolic reactions, while NADP⁺/NADPH is used in anabolic reactions.

Mineral and cofactors

Inorganic elements play critical roles in metabolism; some are abundant (e.g. sodium and potassium) while others function at minute concentrations. About 99% of a human's body weight is made up of the elements carbon, nitrogen, calcium, sodium, chlorine, potassium, hydrogen, phosphorus, oxygen and sulfur. Organic compounds (proteins, lipids and carbohydrates) contain the majority of the carbon and nitrogen; most of the oxygen and hydrogen is present as water.

The abundant inorganic elements act as electrolytes. The most important ions are sodium, potassium, calcium, magnesium, chloride, phosphate and the organic ion bicarbonate. The maintenance of precise ion gradients across cell membranes maintains osmotic pressure and pH. Ions are also critical for nerve and muscle function, as action potentials in these tissues are produced by the exchange of electrolytes between the extracellular fluid and the cell's fluid, the cytosol. Electrolytes enter and leave cells through proteins in the cell membrane called ion channels. For example, muscle contraction depends upon the movement of calcium, sodium and potassium through ion channels in the cell membrane and T-tubules.

Transition metals are usually present as trace elements in organisms, with zinc and iron being most abundant of those. Metal cofactors are bound tightly to specific sites in proteins; although enzyme cofactors can be modified during catalysis, they always return to their original state by the end of the reaction catalyzed. Metal micronutrients are taken up into organisms by specific transporters and bind to storage proteins such as ferritin or metallothionein when not in use.

Catabolism

Catabolism is the set of metabolic processes that break down large molecules. These include breaking down and oxidizing food molecules. The purpose of the catabolic reactions is to provide the energy and components needed by anabolic reactions which build molecules. The exact nature of these catabolic reactions differ from organism to organism, and organisms can be classified based on their sources of energy, hydrogen, and carbon (their primary nutritional groups), as shown in the table below. Organic molecules are used as a source of hydrogen atoms or electrons by organotrophs, while lithotrophs use inorganic substrates. Whereas phototrophs convert sunlight to chemical energy, chemotrophs depend on redox reactions that involve the transfer of electrons from reduced donor molecules such as organic molecules, hydrogen, hydrogen sulfide or ferrous ions to oxygen, nitrate or sulfate. In animals, these reactions involve complex organic molecules that are broken down to simpler molecules, such as carbon dioxide and water. Photosynthetic organisms, such as plants and cyanobacteria, use similar electron-transfer reactions to store energy absorbed from sunlight.

Classification of organisms based on their metabolism
Energy source	sunlight	photo-			-troph
	molecules	chemo-
Hydrogen or electron donor	organic compound		organo-
	inorganic compound		litho-
Carbon source	organic compound			hetero-
	inorganic compound			auto-

The most common set of catabolic reactions in animals can be separated into three main stages. In the first stage, large organic molecules, such as proteins, polysaccharides or lipids, are digested into their smaller components outside cells. Next, these smaller molecules are taken up by cells and converted to smaller molecules, usually acetyl coenzyme A (acetyl-CoA), which releases some energy. Finally, the acetyl group on acetyl-CoA is oxidized to water and carbon dioxide in the citric acid cycle and electron transport chain, releasing more energy while reducing the coenzyme nicotinamide adenine dinucleotide (NAD⁺) into NADH.

Digestion

Macromolecules cannot be directly processed by cells. Macromolecules must be broken into smaller units before they can be used in cell metabolism. Different classes of enzymes are used to digest these polymers. These digestive enzymes include proteases that digest proteins into amino acids, as well as glycoside hydrolases that digest polysaccharides into simple sugars known as monosaccharides.

Microbes simply secrete digestive enzymes into their surroundings, while animals only secrete these enzymes from specialized cells in their guts, including the stomach and pancreas, and in salivary glands. The amino acids or sugars released by these extracellular enzymes are then pumped into cells by active transport proteins.

Energy from organic compounds

Carbohydrate catabolism is the breakdown of carbohydrates into smaller units. Carbohydrates are usually taken into cells after they have been digested into monosaccharides such as glucose and fructose. Once inside, the major route of breakdown is glycolysis, in which glucose is converted into pyruvate. This process generates the energy-conveying molecule NADH from NAD⁺, and generates ATP from ADP for use in powering many processes within the cell. Pyruvate is an intermediate in several metabolic pathways, but the majority is converted to acetyl-CoA and fed into the citric acid cycle, which enables more ATP production by means of oxidative phosphorylation. This oxidation consumes molecular oxygen and releases water and the waste product carbon dioxide. When oxygen is lacking, or when pyruvate is temporarily produced faster than it can be consumed by the citric acid cycle (as in intense muscular exertion), pyruvate is converted to lactate by the enzyme lactate dehydrogenase, a process that also oxidizes NADH back to NAD⁺ for re-use in further glycolysis, allowing energy production to continue. The lactate is later converted back to pyruvate for ATP production where energy is needed, or back to glucose in the Cori cycle. An alternative route for glucose breakdown is the pentose phosphate pathway, which produces less energy but supports anabolism (biomolecule synthesis). This pathway reduces the coenzyme NADP⁺ to NADPH and produces pentose compounds such as ribose 5-phosphate for synthesis of many biomolecules such as nucleotides and aromatic amino acids.

Fats are catabolized by hydrolysis to free fatty acids and glycerol. The glycerol enters glycolysis and the fatty acids are broken down by beta oxidation to release acetyl-CoA, which then is fed into the citric acid cycle. Fatty acids release more energy upon oxidation than carbohydrates. Steroids are also broken down by some bacteria in a process similar to beta oxidation, and this breakdown process involves the release of significant amounts of acetyl-CoA, propionyl-CoA, and pyruvate, which can all be used by the cell for energy. M. tuberculosis can also grow on the lipid cholesterol as a sole source of carbon, and genes involved in the cholesterol-use pathway(s) have been validated as important during various stages of the infection lifecycle of M. tuberculosis.

Amino acids are either used to synthesize proteins and other biomolecules, or oxidized to urea and carbon dioxide to produce energy. The oxidation pathway starts with the removal of the amino group by a transaminase. The amino group is fed into the urea cycle, leaving a deaminated carbon skeleton in the form of a keto acid. Several of these keto acids are intermediates in the citric acid cycle, for example α-ketoglutarate formed by deamination of glutamate. The glucogenic amino acids can also be converted into glucose, through gluconeogenesis.

Energy transformations

Oxidative phosphorylation

In oxidative phosphorylation, the electrons removed from organic molecules in areas such as the citric acid cycle are transferred to oxygen and the energy released is used to make ATP. This is done in eukaryotes by a series of proteins in the membranes of mitochondria called the electron transport chain. In prokaryotes, these proteins are found in the cell's inner membrane. These proteins use the energy from reduced molecules like NADH to pump protons across a membrane.

Mechanism of ATP synthase. ATP is shown in red, ADP and phosphate in pink and the rotating stalk subunit in black.

Pumping protons out of the mitochondria creates a proton concentration difference across the membrane and generates an electrochemical gradient. This force drives protons back into the mitochondrion through the base of an enzyme called ATP synthase. The flow of protons makes the stalk subunit rotate, causing the active site of the synthase domain to change shape and phosphorylate adenosine diphosphate—turning it into ATP.

Energy from inorganic compounds

Chemolithotrophy is a type of metabolism found in prokaryotes where energy is obtained from the oxidation of inorganic compounds. These organisms can use hydrogen, reduced sulfur compounds (such as sulfide, hydrogen sulfide and thiosulfate), ferrous iron (Fe(II)) or ammonia as sources of reducing power and they gain energy from the oxidation of these compounds. These microbial processes are important in global biogeochemical cycles such as acetogenesis, nitrification and denitrification and are critical for soil fertility.

Energy from light

The energy in sunlight is captured by plants, cyanobacteria, purple bacteria, green sulfur bacteria and some protists. This process is often coupled to the conversion of carbon dioxide into organic compounds, as part of photosynthesis, which is discussed below. The energy capture and carbon fixation systems can, however, operate separately in prokaryotes, as purple bacteria and green sulfur bacteria can use sunlight as a source of energy, while switching between carbon fixation and the fermentation of organic compounds.

In many organisms, the capture of solar energy is similar in principle to oxidative phosphorylation, as it involves the storage of energy as a proton concentration gradient. This proton motive force then drives ATP synthesis. The electrons needed to drive this electron transport chain come from light-gathering proteins called photosynthetic reaction centres. Reaction centers are classified into two types depending on the nature of photosynthetic pigment present, with most photosynthetic bacteria only having one type, while plants and cyanobacteria have two.

In plants, algae, and cyanobacteria, photosystem II uses light energy to remove electrons from water, releasing oxygen as a waste product. The electrons then flow to the cytochrome b6f complex, which uses their energy to pump protons across the thylakoid membrane in the chloroplast. These protons move back through the membrane as they drive the ATP synthase, as before. The electrons then flow through photosystem I and can then be used to reduce the coenzyme NADP⁺. This coenzyme can enter the Calvin cycle or be recycled for further ATP generation.

Anabolism

Anabolism is the set of constructive metabolic processes where the energy released by catabolism is used to synthesize complex molecules. In general, the complex molecules that make up cellular structures are constructed step-by-step from smaller and simpler precursors. Anabolism involves three basic stages. First, the production of precursors such as amino acids, monosaccharides, isoprenoids and nucleotides, secondly, their activation into reactive forms using energy from ATP, and thirdly, the assembly of these precursors into complex molecules such as proteins, polysaccharides, lipids and nucleic acids.

Anabolism in organisms can be different according to the source of constructed molecules in their cells. Autotrophs such as plants can construct the complex organic molecules in their cells such as polysaccharides and proteins from simple molecules like carbon dioxide and water. Heterotrophs, on the other hand, require a source of more complex substances, such as monosaccharides and amino acids, to produce these complex molecules. Organisms can be further classified by ultimate source of their energy: photoautotrophs and photoheterotrophs obtain energy from light, whereas chemoautotrophs and chemoheterotrophs obtain energy from oxidation reactions.

Carbon fixation

Photosynthesis is the synthesis of carbohydrates from sunlight and carbon dioxide (CO₂). In plants, cyanobacteria and algae, oxygenic photosynthesis splits water, with oxygen produced as a waste product. This process uses the ATP and NADPH produced by the photosynthetic reaction centres, as described above, to convert CO₂ into glycerate 3-phosphate, which can then be converted into glucose. This carbon-fixation reaction is carried out by the enzyme RuBisCO as part of the Calvin–Benson cycle. Three types of photosynthesis occur in plants, C3 carbon fixation, C4 carbon fixation and CAM photosynthesis. These differ by the route that carbon dioxide takes to the Calvin cycle, with C3 plants fixing CO₂ directly, while C4 and CAM photosynthesis incorporate the CO₂ into other compounds first, as adaptations to deal with intense sunlight and dry conditions.

In photosynthetic prokaryotes the mechanisms of carbon fixation are more diverse. Here, carbon dioxide can be fixed by the Calvin–Benson cycle, a reversed citric acid cycle, or the carboxylation of acetyl-CoA. Prokaryotic chemoautotrophs also fix CO₂ through the Calvin–Benson cycle, but use energy from inorganic compounds to drive the reaction.

Carbohydrates and glycans

In carbohydrate anabolism, simple organic acids can be converted into monosaccharides such as glucose and then used to assemble polysaccharides such as starch. The generation of glucose from compounds like pyruvate, lactate, glycerol, glycerate 3-phosphate and amino acids is called gluconeogenesis. Gluconeogenesis converts pyruvate to glucose-6-phosphate through a series of intermediates, many of which are shared with glycolysis. However, this pathway is not simply glycolysis run in reverse, as several steps are catalyzed by non-glycolytic enzymes. This is important as it allows the formation and breakdown of glucose to be regulated separately, and prevents both pathways from running simultaneously in a futile cycle.

Although fat is a common way of storing energy, in vertebrates such as humans the fatty acids in these stores cannot be converted to glucose through gluconeogenesis as these organisms cannot convert acetyl-CoA into pyruvate; plants do, but animals do not, have the necessary enzymatic machinery. As a result, after long-term starvation, vertebrates need to produce ketone bodies from fatty acids to replace glucose in tissues such as the brain that cannot metabolize fatty acids. In other organisms such as plants and bacteria, this metabolic problem is solved using the glyoxylate cycle, which bypasses the decarboxylation step in the citric acid cycle and allows the transformation of acetyl-CoA to oxaloacetate, where it can be used for the production of glucose. Other than fat, glucose is stored in most tissues, as an energy resource available within the tissue through glycogenesis which was usually being used to maintained glucose level in blood.

Polysaccharides and glycans are made by the sequential addition of monosaccharides by glycosyltransferase from a reactive sugar-phosphate donor such as uridine diphosphate glucose (UDP-Glc) to an acceptor hydroxyl group on the growing polysaccharide. As any of the hydroxyl groups on the ring of the substrate can be acceptors, the polysaccharides produced can have straight or branched structures. The polysaccharides produced can have structural or metabolic functions themselves, or be transferred to lipids and proteins by the enzymes oligosaccharyltransferases.

Fatty acids, isoprenoids and sterol

Fatty acids are made by fatty acid synthases that polymerize and then reduce acetyl-CoA units. The acyl chains in the fatty acids are extended by a cycle of reactions that add the acyl group, reduce it to an alcohol, dehydrate it to an alkene group and then reduce it again to an alkane group. The enzymes of fatty acid biosynthesis are divided into two groups: in animals and fungi, all these fatty acid synthase reactions are carried out by a single multifunctional type I protein, while in plant plastids and bacteria separate type II enzymes perform each step in the pathway.

Terpenes and isoprenoids are a large class of lipids that include the carotenoids and form the largest class of plant natural products. These compounds are made by the assembly and modification of isoprene units donated from the reactive precursors isopentenyl pyrophosphate and dimethylallyl pyrophosphate. These precursors can be made in different ways. In animals and archaea, the mevalonate pathway produces these compounds from acetyl-CoA, while in plants and bacteria the non-mevalonate pathway uses pyruvate and glyceraldehyde 3-phosphate as substrates. One important reaction that uses these activated isoprene donors is sterol biosynthesis. Here, the isoprene units are joined to make squalene and then folded up and formed into a set of rings to make lanosterol. Lanosterol can then be converted into other sterols such as cholesterol and ergosterol.

Proteins

Organisms vary in their ability to synthesize the 20 common amino acids. Most bacteria and plants can synthesize all twenty, but mammals can only synthesize eleven nonessential amino acids, so nine essential amino acids must be obtained from food. Some simple parasites, such as the bacteria Mycoplasma pneumoniae, lack all amino acid synthesis and take their amino acids directly from their hosts. All amino acids are synthesized from intermediates in glycolysis, the citric acid cycle, or the pentose phosphate pathway. Nitrogen is provided by glutamate and glutamine. Nonessensial amino acid synthesis depends on the formation of the appropriate alpha-keto acid, which is then transaminated to form an amino acid.

Amino acids are made into proteins by being joined in a chain of peptide bonds. Each different protein has a unique sequence of amino acid residues: this is its primary structure. Just as the letters of the alphabet can be combined to form an almost endless variety of words, amino acids can be linked in varying sequences to form a huge variety of proteins. Proteins are made from amino acids that have been activated by attachment to a transfer RNA molecule through an ester bond. This aminoacyl-tRNA precursor is produced in an ATP-dependent reaction carried out by an aminoacyl tRNA synthetase. This aminoacyl-tRNA is then a substrate for the ribosome, which joins the amino acid onto the elongating protein chain, using the sequence information in a messenger RNA.

Nucleotide synthesis and salvage

Nucleotides are made from amino acids, carbon dioxide and formic acid in pathways that require large amounts of metabolic energy. Consequently, most organisms have efficient systems to salvage preformed nucleotides. Purines are synthesized as nucleosides (bases attached to ribose). Both adenine and guanine are made from the precursor nucleoside inosine monophosphate, which is synthesized using atoms from the amino acids glycine, glutamine, and aspartic acid, as well as formate transferred from the coenzyme tetrahydrofolate. Pyrimidines, on the other hand, are synthesized from the base orotate, which is formed from glutamine and aspartate.

Xenobiotics and redox metabolism

All organisms are constantly exposed to compounds that they cannot use as foods and that would be harmful if they accumulated in cells, as they have no metabolic function. These potentially damaging compounds are called xenobiotics. Xenobiotics such as synthetic drugs, natural poisons and antibiotics are detoxified by a set of xenobiotic-metabolizing enzymes. In humans, these include cytochrome P450 oxidases, UDP-glucuronosyltransferases, and glutathione S-transferases. This system of enzymes acts in three stages to firstly oxidize the xenobiotic (phase I) and then conjugate water-soluble groups onto the molecule (phase II). The modified water-soluble xenobiotic can then be pumped out of cells and in multicellular organisms may be further metabolized before being excreted (phase III). In ecology, these reactions are particularly important in microbial biodegradation of pollutants and the bioremediation of contaminated land and oil spills. Many of these microbial reactions are shared with multicellular organisms, but due to the incredible diversity of types of microbes these organisms are able to deal with a far wider range of xenobiotics than multicellular organisms, and can degrade even persistent organic pollutants such as organochloride compounds.

A related problem for aerobic organisms is oxidative stress. Here, processes including oxidative phosphorylation and the formation of disulfide bonds during protein folding produce reactive oxygen species such as hydrogen peroxide. These damaging oxidants are removed by antioxidant metabolites such as glutathione and enzymes such as catalases and peroxidases.

Thermodynamics of living organisms

Living organisms must obey the laws of thermodynamics, which describe the transfer of heat and work. The second law of thermodynamics states that in any isolated system, the amount of entropy (disorder) cannot decrease. Although living organisms' amazing complexity appears to contradict this law, life is possible as all organisms are open systems that exchange matter and energy with their surroundings. Living systems are not in equilibrium, but instead are dissipative systems that maintain their state of high complexity by causing a larger increase in the entropy of their environments. The metabolism of a cell achieves this by coupling the spontaneous processes of catabolism to the non-spontaneous processes of anabolism. In thermodynamic terms, metabolism maintains order by creating disorder.

Regulation and control

As the environments of most organisms are constantly changing, the reactions of metabolism must be finely regulated to maintain a constant set of conditions within cells, a condition called homeostasis. Metabolic regulation also allows organisms to respond to signals and interact actively with their environments. Two closely linked concepts are important for understanding how metabolic pathways are controlled. Firstly, the regulation of an enzyme in a pathway is how its activity is increased and decreased in response to signals. Secondly, the control exerted by this enzyme is the effect that these changes in its activity have on the overall rate of the pathway (the flux through the pathway). For example, an enzyme may show large changes in activity (i.e. it is highly regulated) but if these changes have little effect on the flux of a metabolic pathway, then this enzyme is not involved in the control of the pathway.

There are multiple levels of metabolic regulation. In intrinsic regulation, the metabolic pathway self-regulates to respond to changes in the levels of substrates or products; for example, a decrease in the amount of product can increase the flux through the pathway to compensate. This type of regulation often involves allosteric regulation of the activities of multiple enzymes in the pathway. Extrinsic control involves a cell in a multicellular organism changing its metabolism in response to signals from other cells. These signals are usually in the form of water-soluble messengers such as hormones and growth factors and are detected by specific receptors on the cell surface. These signals are then transmitted inside the cell by second messenger systems that often involved the phosphorylation of proteins.

A very well understood example of extrinsic control is the regulation of glucose metabolism by the hormone insulin. Insulin is produced in response to rises in blood glucose levels. Binding of the hormone to insulin receptors on cells then activates a cascade of protein kinases that cause the cells to take up glucose and convert it into storage molecules such as fatty acids and glycogen. The metabolism of glycogen is controlled by activity of phosphorylase, the enzyme that breaks down glycogen, and glycogen synthase, the enzyme that makes it. These enzymes are regulated in a reciprocal fashion, with phosphorylation inhibiting glycogen synthase, but activating phosphorylase. Insulin causes glycogen synthesis by activating protein phosphatases and producing a decrease in the phosphorylation of these enzymes.

Evolution

The central pathways of metabolism described above, such as glycolysis and the citric acid cycle, are present in all three domains of living things and were present in the last universal common ancestor. This universal ancestral cell was prokaryotic and probably a methanogen that had extensive amino acid, nucleotide, carbohydrate and lipid metabolism. The retention of these ancient pathways during later evolution may be the result of these reactions having been an optimal solution to their particular metabolic problems, with pathways such as glycolysis and the citric acid cycle producing their end products highly efficiently and in a minimal number of steps. The first pathways of enzyme-based metabolism may have been parts of purine nucleotide metabolism, while previous metabolic pathways were a part of the ancient RNA world.

Many models have been proposed to describe the mechanisms by which novel metabolic pathways evolve. These include the sequential addition of novel enzymes to a short ancestral pathway, the duplication and then divergence of entire pathways as well as the recruitment of pre-existing enzymes and their assembly into a novel reaction pathway. The relative importance of these mechanisms is unclear, but genomic studies have shown that enzymes in a pathway are likely to have a shared ancestry, suggesting that many pathways have evolved in a step-by-step fashion with novel functions created from pre-existing steps in the pathway. An alternative model comes from studies that trace the evolution of proteins' structures in metabolic networks, this has suggested that enzymes are pervasively recruited, borrowing enzymes to perform similar functions in different metabolic pathways (evident in the MANET database) These recruitment processes result in an evolutionary enzymatic mosaic. A third possibility is that some parts of metabolism might exist as "modules" that can be reused in different pathways and perform similar functions on different molecules.

As well as the evolution of new metabolic pathways, evolution can also cause the loss of metabolic functions. For example, in some parasites metabolic processes that are not essential for survival are lost and preformed amino acids, nucleotides and carbohydrates may instead be scavenged from the host. Similar reduced metabolic capabilities are seen in endosymbiotic organisms.

Investigation and manipulation

Classically, metabolism is studied by a reductionist approach that focuses on a single metabolic pathway. Particularly valuable is the use of radioactive tracers at the whole-organism, tissue and cellular levels, which define the paths from precursors to final products by identifying radioactively labelled intermediates and products. The enzymes that catalyze these chemical reactions can then be purified and their kinetics and responses to inhibitors investigated. A parallel approach is to identify the small molecules in a cell or tissue; the complete set of these molecules is called the metabolome. Overall, these studies give a good view of the structure and function of simple metabolic pathways, but are inadequate when applied to more complex systems such as the metabolism of a complete cell.

An idea of the complexity of the metabolic networks in cells that contain thousands of different enzymes is given by the figure showing the interactions between just 43 proteins and 40 metabolites to the right: the sequences of genomes provide lists containing anything up to 26.500 genes. However, it is now possible to use this genomic data to reconstruct complete networks of biochemical reactions and produce more holistic mathematical models that may explain and predict their behavior. These models are especially powerful when used to integrate the pathway and metabolite data obtained through classical methods with data on gene expression from proteomic and DNA microarray studies. Using these techniques, a model of human metabolism has now been produced, which will guide future drug discovery and biochemical research. These models are now used in network analysis, to classify human diseases into groups that share common proteins or metabolites.

Bacterial metabolic networks are a striking example of bow-tie organization, an architecture able to input a wide range of nutrients and produce a large variety of products and complex macromolecules using a relatively few intermediate common currencies.

A major technological application of this information is metabolic engineering. Here, organisms such as yeast, plants or bacteria are genetically modified to make them more useful in biotechnology and aid the production of drugs such as antibiotics or industrial chemicals such as 1,3-propanediol and shikimic acid. These genetic modifications usually aim to reduce the amount of energy used to produce the product, increase yields and reduce the production of wastes.

History

The term metabolism is derived from the Ancient Greek word μεταβολή—"metabole" for "a change" which is derived from μεταβάλλειν—"metaballein", meaning "to change"

Greek philosophy

Aristotle's The Parts of Animals sets out enough details of his views on metabolism for an open flow model to be made. He believed that at each stage of the process, materials from food were transformed, with heat being released as the classical element of fire, and residual materials being excreted as urine, bile, or faeces.

Ibn al-Nafis described metabolism in his 1260 AD work titled Al-Risalah al-Kamiliyyah fil Siera al-Nabawiyyah (The Treatise of Kamil on the Prophet's Biography) which included the following phrase "Both the body and its parts are in a continuous state of dissolution and nourishment, so they are inevitably undergoing permanent change."

Application of the scientific method and Modern metabolic theories

The history of the scientific study of metabolism spans several centuries and has moved from examining whole animals in early studies, to examining individual metabolic reactions in modern biochemistry. The first controlled experiments in human metabolism were published by Santorio Santorio in 1614 in his book Ars de statica medicina. He described how he weighed himself before and after eating, sleep, working, sex, fasting, drinking, and excreting. He found that most of the food he took in was lost through what he called "insensible perspiration".

Santorio Santorio in his steelyard balance, from *Ars de statica medicina*, first published 1614

In these early studies, the mechanisms of these metabolic processes had not been identified and a vital force was thought to animate living tissue. In the 19th century, when studying the fermentation of sugar to alcohol by yeast, Louis Pasteur concluded that fermentation was catalyzed by substances within the yeast cells he called "ferments". He wrote that "alcoholic fermentation is an act correlated with the life and organization of the yeast cells, not with the death or putrefaction of the cells." This discovery, along with the publication by Friedrich Wöhler in 1828 of a paper on the chemical synthesis of urea, and is notable for being the first organic compound prepared from wholly inorganic precursors. This proved that the organic compounds and chemical reactions found in cells were no different in principle than any other part of chemistry.

It was the discovery of enzymes at the beginning of the 20th century by Eduard Buchner that separated the study of the chemical reactions of metabolism from the biological study of cells, and marked the beginnings of biochemistry. The mass of biochemical knowledge grew rapidly throughout the early 20th century. One of the most prolific of these modern biochemists was Hans Krebs who made huge contributions to the study of metabolism. He discovered the urea cycle and later, working with Hans Kornberg, the citric acid cycle and the glyoxylate cycle. Modern biochemical research has been greatly aided by the development of new techniques such as chromatography, X-ray diffraction, NMR spectroscopy, radioisotopic labelling, electron microscopy and molecular dynamics simulations. These techniques have allowed the discovery and detailed analysis of the many molecules and metabolic pathways in cells.

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Saturday, August 31, 2024

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