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Monday, August 3, 2026

Complex system

From Wikipedia, the free encyclopedia

A complex system is a system composed of many components that interact with one another. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as a power grid, transportation or communication systems, complex software and electronic systems, social and economic organizations (like cities), an ecosystem, a living cell, and, ultimately, for some authors, the entire universe.

The behavior of a complex system is intrinsically difficult to model due to the dependencies, competitions, relationships, and other types of interactions between their parts or between a given system and its environment. Systems that are "complex" have distinct properties that arise from these relationships, such as nonlinearity, emergence, spontaneous order, adaptation, and feedback loops, among others. Because such systems appear in a wide variety of fields, the commonalities among them have become the topic of their independent area of research. In many cases, it is useful to represent such a system as a network (graph) where the nodes represent the components and links represent their interactions.

The term complex systems often refers to the study of complex systems, which is an approach to science that investigates how relationships between a system's parts give rise to its collective behaviors and how the system interacts and forms relationships with its environment. The study of complex systems regards collective, or system-wide, behaviors as the fundamental object of study; for this reason, complex systems can be understood as an alternative paradigm to reductionism, which attempts to explain systems in terms of their constituent parts and the individual interactions between them.

As an interdisciplinary domain, complex systems draw contributions from many different fields, such as the study of self-organization and critical phenomena from physics, of spontaneous order from the social sciences, chaos from mathematics, adaptation from biology, and many others. Complex systems is therefore often used as a broad term encompassing a research approach to problems in many diverse disciplines, including statistical physics, information theory, nonlinear dynamics, anthropology, computer science, meteorology, sociology, economics, psychology, and biology.

Types of systems

Complex systems can be:

  • Complex adaptive systems which have the capacity to change,
  • Polycentric systems "where many elements are capable of making mutual adjustments for ordering their relationships with one another within a general system of rules where each element acts with independence of other elements",
  • Disorganised systems which involve localized interactions of multiple entities that do not form a coherent whole. Disorganised systems are linked to self-organisation processes.
  • Hierarchical systems which are analyzable into successive sets of subsystems. They can also be called nested or embedded systems.
  • Cybernetic systems which involve information feedback loops.

Key concepts

Adaptation

Complex adaptive systems are special cases of complex systems that are adaptive in that they have the capacity to change and learn from experience. Examples of complex adaptive systems include the international trade markets, social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, cities, manufacturing businesses and any human social group-based endeavor in a cultural and social system such as political parties or communities.

Decomposability

A system is decomposable if the parts of the system (subsystems) are independent from each other, for example the model of a perfect gas considers the relations among molecules negligible.

In a nearly decomposable system, the interactions between subsystems are weak but not negligible; this is often the case in social systems. Conceptually, a system is nearly decomposable if the variables composing it can be separated into classes and subclasses, if these variables are independent for many functions but affect each other, and if the whole system is greater than the parts.

Features

Complex systems may have the following features:

Complex systems may be open
Complex systems are usually open systems – that is, they exist in a thermodynamic gradient and dissipate energy. In other words, complex systems are frequently far from energetic equilibrium: but despite this flux, there may be pattern stability, see synergetics.
Complex systems may exhibit critical transitions
Graphical representation of alternative stable states and the direction of critical slowing down prior to a critical transition (taken from Lever et al. 2020). Top panels (a) indicate stability landscapes at different conditions. Middle panels (b) indicate the rates of change akin to the slope of the stability landscapes, and bottom panels (c) indicate a recovery from a perturbation towards the system's future state (c.I) and in another direction (c.II).
Critical transitions are abrupt shifts in the state of ecosystems, the climate, financial and economic systems or other complex systems that may occur when changing conditions pass a critical or bifurcation point. The 'direction of critical slowing down' in a system's state space may be indicative of a system's future state after such transitions when delayed negative feedbacks leading to oscillatory or other complex dynamics are weak.
Complex systems may be nested
The components of a complex system may themselves be complex systems. For example, an economy is made up of organisations, which are made up of people, which are made up of cells – all of which are complex systems. The arrangement of interactions within complex bipartite networks may be nested as well. More specifically, bipartite ecological and organisational networks of mutually beneficial interactions were found to have a nested structure. This structure promotes indirect facilitation and a system's capacity to persist under increasingly harsh circumstances as well as the potential for large-scale systemic regime shifts.
Dynamic network of multiplicity
As well as coupling rules, the dynamic network of a complex system is important. Small-world or scale-free networks which have many local interactions and a smaller number of inter-area connections are often employed. Natural complex systems often exhibit such topologies. In the human cortex for example, we see dense local connectivity and a few very long axon projections between regions inside the cortex and to other brain regions.
Gosper's Glider Gun creating "gliders" in the cellular automaton Conway's Game of Life
May produce emergent phenomena
Complex systems may exhibit behaviors that are emergent, which is to say that while the results may be sufficiently determined by the activity of the systems' basic constituents, they may have properties that can only be studied at a higher level. For example, empirical food webs display regular, scale-invariant features across aquatic and terrestrial ecosystems when studied at the level of clustered 'trophic' species. Another example is offered by the termites in a mound which have physiology, biochemistry and biological development at one level of analysis, whereas their social behavior and mound building is a property that emerges from the collection of termites and needs to be analyzed at a different level.
Relationships are non-linear
In practical terms, this means a small perturbation may cause a large effect (see butterfly effect), a proportional effect, or even no effect at all. In linear systems, the effect is always directly proportional to cause. See nonlinearity.
Relationships contain feedback loops
Both negative (damping) and positive (amplifying) feedback are always found in complex systems. The effects of an element's behavior are fed back in such a way that the element itself is altered.

History

In 1948, Dr. Warren Weaver published an essay on "Science and Complexity", exploring the diversity of problem types by contrasting problems of simplicity, disorganized complexity, and organized complexity. Weaver described these as "problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole."

While the explicit study of complex systems dates at least to the 1970s, the first research institute focused on complex systems, the Santa Fe Institute, was founded in 1984. Early Santa Fe Institute participants included physics Nobel laureates Murray Gell-Mann and Philip Anderson, economics Nobel laureate Kenneth Arrow, and Manhattan Project scientists George Cowan and Herb Anderson. Today, there are over 50 institutes and research centers focusing on complex systems.

Since the late 1990s, the interest of mathematical physicists in researching economic phenomena has been on the rise. The proliferation of cross-disciplinary research with the application of solutions originated from the physics epistemology has entailed a gradual paradigm shift in the theoretical articulations and methodological approaches in economics, primarily in financial economics. The development has resulted in the emergence of a new branch of discipline, namely "econophysics", which is broadly defined as a cross-discipline that applies statistical physics methodologies which are mostly based on the complex systems theory and the chaos theory for economics analysis.

The 2021 Nobel Prize in Physics was awarded to Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi for their work to understand complex systems. Their work was used to create more accurate computer models of the effect of global warming on the Earth's climate.

Applications

Complexity in practice

The traditional approach to dealing with complexity is to reduce or constrain it. Typically, this involves compartmentalization: dividing a large system into separate parts. Organizations, for instance, divide their work into departments that each deal with separate issues. Engineering systems are often designed using modular components. However, modular designs become susceptible to failure when issues arise that bridge the divisions.

Complexity of cities

Jane Jacobs described cities as being a problem in organized complexity in 1961, citing Dr. Weaver's 1948 essay. As an example, she explains how an abundance of factors interplay into how various urban spaces lead to a diversity of interactions, and how changing those factors can change how the space is used, and how well the space supports the functions of the city. She further illustrates how cities have been severely damaged when approached as a problem in simplicity by replacing organized complexity with simple and predictable spaces, such as Le Corbusier's "Radiant City" and Ebenezer Howard's "Garden City". Since then, others have written at length on the complexity of cities.

Complexity economics

Over the last decades, within the emerging field of complexity economics, new predictive tools have been developed to explain economic growth. Such is the case with the models built by the Santa Fe Institute in 1989 and the more recent economic complexity index (ECI), introduced by the MIT physicist Cesar A. Hidalgo and the Harvard economist Ricardo Hausmann.

Recurrence quantification analysis has been employed to detect the characteristic of business cycles and economic development. To this end, Orlando et al. developed the so-called recurrence quantification correlation index (RQCI) to test correlations of RQA on a sample signal and then investigated the application to business time series. The said index has been proven to detect hidden changes in time series. Further, Orlando et al., over an extensive dataset, shown that recurrence quantification analysis may help in anticipating transitions from laminar (i.e. regular) to turbulent (i.e. chaotic) phases such as USA GDP in 1949, 1953, etc. Last but not least, it has been demonstrated that recurrence quantification analysis can detect differences between macroeconomic variables and highlight hidden features of economic dynamics.

Complexity and education

Focusing on issues of student persistence with their studies, Forsman, Moll and Linder explore the "viability of using complexity science as a frame to extend methodological applications for physics education research", finding that "framing a social network analysis within a complexity science perspective offers a new and powerful applicability across a broad range of PER topics".

Complexity in healthcare research and practice

Healthcare systems are prime examples of complex systems, characterized by interactions among diverse stakeholders, such as patients, providers, policymakers, and researchers, across various sectors like health, government, community, and education. These systems demonstrate properties like non-linearity, emergence, adaptation, and feedback loops. Complexity science in healthcare frames knowledge translation as a dynamic and interconnected network of processes—problem identification, knowledge creation, synthesis, implementation, and evaluation—rather than a linear or cyclical sequence. Such approaches emphasize the importance of understanding and leveraging the interactions within and between these processes and stakeholders to optimize the creation and movement of knowledge. By acknowledging the complex, adaptive nature of healthcare systems, complexity science advocates for continuous stakeholder engagement, transdisciplinary collaboration, and flexible strategies to effectively translate research into practice.

Complexity and biology

Complexity science has been applied to living organisms, and in particular to biological systems. Within the emerging field of fractal physiology, bodily signals, such as heart rate or brain activity, are characterized using entropy or fractal indices. The goal is often to assess the state and the health of the underlying system, and diagnose potential disorders and illnesses.

Complexity and chaos theory

Complex systems theory is related to chaos theory, which in turn has its origins more than a century ago in the work of the French mathematician Henri PoincarĂ©. Chaos is sometimes viewed as extremely complicated information, rather than as an absence of order. Chaotic systems remain deterministic, though their long-term behavior can be difficult to predict with any accuracy. With perfect knowledge of the initial conditions and the relevant equations describing the chaotic system's behavior, one can theoretically make perfectly accurate predictions of the system, though in practice this is impossible to do with arbitrary accuracy.

The emergence of complex systems theory shows a domain between deterministic order and randomness which is complex. This is referred to as the "edge of chaos".

A plot of the Lorenz attractor

When one analyzes complex systems, sensitivity to initial conditions, for example, is not an issue as important as it is within chaos theory, in which it prevails. As stated by Colander, the study of complexity is the opposite of the study of chaos. Complexity is about how a huge number of extremely complicated and dynamic sets of relationships can generate some simple behavioral patterns, whereas chaotic behavior, in the sense of deterministic chaos, is the result of a relatively small number of non-linear interactions. For recent examples in economics and business see Stoop et al. who discussed Android's market position, Orlando who explained the corporate dynamics in terms of mutual synchronization and chaos regularization of bursts in a group of chaotically bursting cells and Orlando et al. who modelled financial data (Financial Stress Index, swap and equity, emerging and developed, corporate and government, short and long maturity) with a low-dimensional deterministic model.

Therefore, the main difference between chaotic systems and complex systems is their history. Chaotic systems do not rely on their history as complex ones do. Chaotic behavior pushes a system in equilibrium into chaotic order, which means, in other words, out of what we traditionally define as 'order'. On the other hand, complex systems evolve far from equilibrium at the edge of chaos. They evolve at a critical state built up by a history of irreversible and unexpected events, which physicist Murray Gell-Mann called "an accumulation of frozen accidents". In a sense chaotic systems can be regarded as a subset of complex systems distinguished precisely by this absence of historical dependence. Many real complex systems are, in practice and over long but finite periods, robust. However, they do possess the potential for radical qualitative change of kind whilst retaining systemic integrity. Metamorphosis serves as perhaps more than a metaphor for such transformations.

Complexity and network science

A complex system is usually composed of many components and their interactions. Such a system can be represented by a network where nodes represent the components and links represent their interactions. For example, the Internet can be represented as a network composed of nodes (computers) and links (direct connections between computers). Other examples of complex networks include social networks, financial institution interdependencies, airline networks, and biological networks.

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Systems science

From Wikipedia, the free encyclopedia
Impression of systems thinking about society

Systems science, also referred to as systems research or simply systems, is a transdisciplinary field that is concerned with understanding simple and complex systems in nature and society, which leads to the advancements of formal, natural, social, and applied attributions throughout engineering, technology, and science itself.

To systems scientists, the world can be understood as a system of systems. The field aims to develop transdisciplinary foundations that are applicable in a variety of areas, such as psychology, biology, medicine, communication, business, technology, computer science, engineering, and social sciences.

Themes commonly stressed in system science are (a) holistic view, (b) interaction between a system and its embedding environment, and (c) complex (often subtle) trajectories of dynamic behavior that sometimes are stable (and thus reinforcing), while at various 'boundary conditions' can become wildly unstable (and thus destructive). Concerns about Earth-scale biosphere/geosphere dynamics is an example of the nature of problems to which systems science seeks to contribute meaningful insights.

Associated fields

The systems sciences are a broad array of fields. One way of conceiving of these is in three groups: fields that have developed systems ideas primarily through theory; those that have done so primarily through practical engagements with problem situations; and those that have applied ideas for other disciplines.

Theoretical fields

Chaos and dynamical systems

Complexity

Control theory

Cybernetics

Information theory

General systems theory

Hierarchy Theory

Practical fields

Critical systems thinking

Operations research and management science

Soft systems methodology

The soft systems methodology was developed in England by academics at the University of Lancaster Systems Department through a ten-year action research programme. The main contributor is Peter Checkland (born 18 December 1930, in Birmingham, UK), a British management scientist and emeritus professor of systems at Lancaster University.

Systems analysis

Systems analysis branch of systems science that analyzes systems, the interactions within those systems, or interaction with its environment, often prior to their automation as computer models. Systems analysis is closely associated with the RAND corporation.

Systemic design

Systemic design integrates methodologies from systems thinking with advanced design practices to address complex, multi-stakeholder situations.

Systems dynamics

System dynamics is an approach to understanding the behavior of complex systems over time. It offers "simulation technique for modeling business and social systems", which deals with internal feedback loops and time delays that affect the behavior of the entire system. What makes using system dynamics different from other approaches to studying complex systems is the use of feedback loops and stocks and flows.

Systems engineering

Systems engineering (SE) is an interdisciplinary field of engineering, that focuses on the development and organization of complex systems. It is the "art and science of creating whole solutions to complex problems", for example: signal processing systems, control systems and communication system, or other forms of high-level modelling and design in specific fields of engineering. Systems Science is foundational to the Embedded Software Development that is founded in the embedded requirements of Systems Engineering.

Applications in other disciplines

Earth system science

Systems biology

Systems chemistry

Systems ecology

Systems psychology

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Cytochrome P450

From Wikipedia, the free encyclopedia
Cytochrome P450
Structure of lanosterol 14α-demethylase (CYP51)

Cytochromes P450 (P450s or CYPs) are a superfamily of enzymes containing heme as a cofactor that mostly, but not exclusively, function as monooxygenases. However, they are not omnipresent; for example, they have not been found in Escherichia coli. In mammals, these enzymes oxidize steroids, fatty acids, xenobiotics, and participate in many biosyntheses. By hydroxylation, CYP450 enzymes convert xenobiotics into hydrophilic derivatives, which are more readily excreted.

P450s are, in general, the terminal oxidase enzymes in electron transfer chains, broadly categorized as P450-containing systems. The term "P450" is derived from the spectrophotometric peak at the wavelength of the absorption maximum of the enzyme (450 nm) when it is in the reduced state and complexed with carbon monoxide. Most P450s require a protein partner to deliver one or more electrons to reduce the iron (and eventually molecular oxygen).

The most common reaction catalyzed by cytochromes P450 is a monooxygenase reaction, e.g., insertion of one atom of oxygen into the aliphatic position of an organic substrate (RH), while the other oxygen atom is reduced to water:

RH + O2 + NADPH + H+ → ROH + H2O + NADP+

Classification

Nomenclature

Genes encoding P450 enzymes, and the enzymes themselves, are designated with the root symbol CYP for the superfamily, followed by a number indicating the gene family, a capital letter indicating the subfamily, and another numeral for the individual gene. The convention is to italicize the name when referring to the gene. For example, CYP2E1 is the gene that encodes the enzyme CYP2E1—one of the enzymes involved in paracetamol (acetaminophen) metabolism. The CYP nomenclature is the official naming convention, although occasionally CYP450 or CYP450 is used synonymously. These names should never be used as according to the nomenclature convention (as they denote a P450 in family number 450). However, some gene or enzyme names for P450s are also referred to by historical names (e.g. P450BM3 for CYP102A1) or functional names, denoting the catalytic activity and the name of the compound used as substrate. Examples include CYP5A1, thromboxane A2 synthase, abbreviated to TBXAS1 (thromboxane A2 synthase 1), and CYP51A1, lanosterol 14-α-demethylase, sometimes unofficially abbreviated to LDM according to its substrate (lanosterol) and activity (demethylation).

The nomenclature guidelines suggest that members of new CYP families share at least 40% amino-acid identity, while members of subfamilies must share at least 55% amino-acid identity. Nomenclature committees assign and track both base gene names (Cytochrome P450 Homepage Archived 2010-06-27 at the Wayback Machine) and allele names (CYP Allele Nomenclature Committee). These similarity-based groupings are frequently recovered in phylogenetic analyses and members generally share features in their catalytic activities. Sometimes the suggested similarity thresholds do not exactly match what phylogenetic patterns show (e.g. a new member that is close to a family but only 39% identical), causing what is known as "family creep" as the similarity threshold is reduced, or the occasional split of families.

There is a universal nomenclature for the assignment of P450 family numbers across the taxonomic groups:

  • CYP1–49, CYP301–CYP499, ...: Animals
    • CYP440–446: Reserved for CYP74 clan members found in animals
  • CYP51–69, CYP501–CYP699, ...: Fungi and Lower Eukaryotes
    • CYP6001–CYP6099: Fungal fusions of P450s with other enzymes, e.g. dioxygenases or peroxygenases or isomerases.
  • CYP71–99, CYP701–CYP999, ...: Plants (Archaeplastida)
  • CYP101–299, ... Bacteria

The taxonomic group blocks are defined for CYP1001–CYP69999 by stretching the taxonomic blocks defined for CYP101–999. For example, CYP3001–4999 and CYP30001–CYP49999 are allocated to animals. The reservations defined under these groups are not stretched. Several databases are available for the tracking of defined P450 family numbers, subfamily letters, and ortholog group numbers, with the intention that not only there is no ambiguity in what each family-subfamily prefix means, but also that identically-named genes across different species are orthologous to each other. As of April 2026, the most complete database is the P450 Atlas (version 1.3.0) covering 11068 families, 26037 subfamilies, 79577 ortholog groups and 164068 example sequences of ortholog groups.

Comparison between many P450 enzymes of different families give rise to the concept of clans, evolutionary grouping of families. The exact similarity cut-off is poorly defined, but it is generally understood that it should derive from a few first-diverging nodes of a phylogenetic tree. Some clans only have one family while others are highly diversified with many families within (e.g. CYP71-clan and CYP85-clan). Tracking the emergence of clans and families across many taxonomic groups paints a vivid picture of the evolution of metabolic capabilities.

By electron transfer system

Based on the nature of the electron transfer proteins, P450s can be classified into several groups:

  • CPR-P450 systems combine a cytochrome P450 reductase (CPR) and a P450 domain.
    • Microsomal P450 systems in which electrons are transferred from NADPH via a CPR (variously CPR, POR, or CYPOR).
    • Also found in bacteria such as the P450meg (CYP106A2) from Bacillus megaterium.
  • Fr/Fd/P450 systems which employ a ferredoxin reductase and a ferredoxin to transfer electrons to P450. A representative is the plant plastid P450cam (CYP101A1) system from the CAM operon for camphor-related substrates.
  • CYB5R/cyb5/P450 systems in which both electrons required by the CYP come from cytochrome b5, which is in turn reduced by cytochrome b5 reductase (CYB5R).
  • P450-only system which do not require external reducing power. Notable ones include thromboxane synthase (CYP5), prostacyclin synthase (CYP8), and CYP74A (allene oxide synthase).

Mechanism

The P450 catalytic cycle
The "Fe(V) intermediate" at the bottom left is a simplification: it is an Fe(IV) with a radical heme ligand.

Structure

The active site of cytochrome P450 contains a heme-iron center. The iron is tethered to the protein via a cysteine thiolate ligand. This cysteine and several flanking residues are highly conserved in known P450s, and have the formal PROSITE signature consensus pattern [FW] - [SGNH] - x - [GD] - {F} - [RKHPT] - {P} - C - [LIVMFAP] - [GAD]. In general, the P450 catalytic cycle proceeds as follows:

Catalytic cycle

  1. Substrate binds in proximity to the heme group, on the side opposite to the axial thiolate. Substrate binding induces a change in the conformation of the active site, often displacing a water molecule from the distal axial coordination position of the heme iron, and changing the state of the heme iron from low-spin to high-spin.
  2. Substrate binding induces electron transfer from NAD(P)H via cytochrome P450 reductase or another associated reductase, converting Fe(III) to Fe(II).
  3. Molecular oxygen binds to the resulting ferrous heme center at the distal axial coordination position, initially giving a dioxygen adduct similar to oxy-myoglobin.
  4. A second electron is transferred, from either cytochrome P450 reductase, ferredoxins, or cytochrome b5, reducing the Fe-O2 adduct to give a short-lived peroxo state.
  5. The peroxo group formed in step 4 is rapidly protonated twice, releasing one molecule of water and forming the highly reactive species referred to as P450 Compound 1 (or just Compound I). This highly reactive intermediate was isolated in 2010, P450 Compound 1 is an iron(IV) oxo (or ferryl) species with an additional oxidizing equivalent delocalized over the porphyrin and thiolate ligands. Evidence for the alternative perferryl iron(V)-oxo is lacking.
Oxygen rebound mechanism utilized by cytochrome P450 for conversion of hydrocarbons to alcohols via the action of "compound I", an iron(IV) oxide bound to a heme radical cation.

Depending on the substrate and enzyme involved, P450 enzymes can catalyze any of a wide variety of reactions. A hypothetical hydroxylation is illustrated. After the hydroxylated product has been released from the active site, the enzyme returns to its original state, with a water molecule returning to occupy the distal coordination position of the iron nucleus.

An alternative route for mono-oxygenation is via the "peroxide shunt" (path "S" in figure). This pathway entails oxidation of the ferric-substrate complex with oxygen-atom donors such as peroxides and hypochlorites. A hypothetical peroxide "XOOH" is shown in the diagram.

Mechanistic details, including the oxygen rebound mechanism, have been investigated with synthetic analogues, consisting of iron oxo heme complexes.

Spectroscopy

Binding of substrate is reflected in the spectral properties of the enzyme, with an increase in absorbance at 390 nm and a decrease at 420 nm. This can be measured by difference spectroscopies and is referred to as the "type I" difference spectrum (see inset graph in figure). Some substrates cause an opposite change in spectral properties, a "reverse type I" spectrum, by processes that are as yet unclear. Inhibitors and certain substrates that bind directly to the heme iron give rise to the type II difference spectrum, with a maximum at 430 nm and a minimum at 390 nm (see inset graph in figure). If no reducing equivalents are available, this complex may remain stable, allowing the degree of binding to be determined from absorbance measurements in vitro C: If carbon monoxide (CO) binds to reduced P450, the catalytic cycle is interrupted. This reaction yields the classic CO difference spectrum with a maximum at 450 nm. However, the interruptive and inhibitory effects of CO varies upon different CYPs such that the CYP3A family is relatively less affected.

Binding site

The conserved sequence of cytochrome P450 is highlighted, depicting how specific amino acid residues are essential for binding to a heme. In cytochrome p450 as seen in Streptomyces antibioticus (PDB code 4XE3), Phe349, Gly352, Ala353, Cys356, and Gly358 represent the conserved domain.

The heme in cytochrome P450 binds to a conserved sequence: FxxGxRxCxG, where "x" denotes some variant amino acid. The cysteine (C) binds iron and arginine (R), forming strong electrostatic interactions with negatively charged side chains of the heme. The glycine (G) residues within the conserved sequence are essential, as their small structure enables surrounding alpha helices to remain in place without interacting with a variant amino acid. Additional conserved motifs are:

  • ExxR (K-helix)
  • AGxDTT (I-helix, oxygen-binding domain)

Other hydroxylation enzymes

Many hydroxylation reactions (insertion of hydroxyl groups) use CYP enzymes, but many other hydroxylases exist. Alpha-ketoglutarate-dependent hydroxylases also rely on an Fe=O intermediate but lack hemes. Methane monooxygenase, which converts methane to methanol, are non-heme iron-and iron-copper-based enzymes.

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Drug interaction

From Wikipedia, the free encyclopedia
Grapefruit juice can act as an enzyme inhibitor, affecting the metabolism of drugs.

In pharmaceutical sciences, drug interactions occur when a drug's mechanism of action is affected by the concomitant administration of substances such as foods, beverages, or other drugs. A popular example of drug–food interaction is the effect of grapefruit on the metabolism of drugs.

Interactions may occur by simultaneous targeting of receptors, directly or indirectly. For example, both Zolpidem and alcohol affect GABAA receptors, and their simultaneous consumption results in the overstimulation of the receptor, which can lead to loss of consciousness. When two drugs affect each other, it is a drug–drug interaction (DDI). The risk of a DDI increases with the number of drugs used.

A large share of elderly people regularly use five or more medications or supplements, with a significant risk of side-effects from drug–drug interactions.

Drug interactions can be of three kinds:

  • additive (the result is simply the expected effect of each drug taken independently),
  • synergistic (combining the drugs leads to a larger effect than expected), or
  • antagonistic (combining the drugs leads to a smaller effect than expected).

It may be difficult to distinguish between synergistic or additive interactions, as individual effects of drugs may vary.

Direct interactions between drugs are also possible and may occur when two drugs are mixed before intravenous injection. For example, mixing thiopentone and suxamethonium can lead to the precipitation of thiopentone.

Interactions based on pharmacodynamics

Pharmacodynamic interactions are the drug–drug interactions that occur at a biochemical level and depend mainly on the biological processes of organisms. These interactions occur due to action on the same targets; for example, the same receptor or signaling pathway.

Effects of the competitive inhibition of an agonist by increases in the concentration of an antagonist. A drug's potency can be affected (the response curve shifted to the right) by the presence of an antagonistic interaction.

Pharmacodynamic interactions can occur on protein receptors. Two drugs can be considered to be homodynamic, if they act on the same receptor. Homodynamic effects include drugs that act as (1) pure agonists, if they bind to the main locus of the receptor, causing a similar effect to that of the main drug, (2) partial agonists if, on binding to a secondary site, they have the same effect as the main drug, but with a lower intensity and (3) antagonists, if they bind directly to the receptor's main locus but their effect is opposite to that of the main drug. These may be competitive antagonists, if they compete with the main drug to bind with the receptor. or uncompetitive antagonists, when the antagonist binds to the receptor irreversibly. The drugs can be considered heterodynamic competitors, if they act on distinct receptor with similar downstream pathways.

The interaction my also occur via signal transduction mechanisms. For example, low blood glucose leads to a release of catecholamines, triggering symptoms that hint the organism to take action, like consuming sugary foods. If a patient is on insulin, which reduces blood sugar, and also beta-blockers, the body is less able to cope with an insulin overdose.

Interactions based on pharmacokinetics

Pharmacokinetics is the field of research studying the chemical and biochemical factors that directly affect dosage and the half-life of drugs in an organism, including absorption, transport, distribution, metabolism and excretion. Compounds may affect any of those process, ultimately interfering with the flux of drugs in the human body, increasing or reducing drug availability.

Based on absorption

Drugs that change intestinal motility may impact the level of other drugs taken. For example, prokinetic agents increase the intestinal motility, which may cause drugs to go through the digestive system too fast, reducing absorption.

The pharmacological modification of pH can affect other compounds. Drugs can be present in ionized or non-ionized forms depending on pKa, and neutral compounds are usually better absorbed by membranes. Medication like antacids can increase pH and inhibit the absorption of other drugs such as zalcitabine, tipranavir and amprenavir. The opposite is more common, with, for example, the antacid cimetidine stimulating the absorption of didanosine. Some resources describe that a gap of two to four hours between taking the two drugs is needed to avoid the interaction.

Factors such as food with high-fat content may also alter the solubility of drugs and impact its absorption. This is the case for oral anticoagulants and avocado. The formation of non-absorbable complexes may occur also via chelation, when cations can make certain drugs harder to absorb, for example between tetracycline or the fluoroquinolones and dairy products, due to the presence of calcium ions. Other drugs bind to proteins. Some drugs such as sucralfate bind to proteins, especially if they have a high bioavailability. For this reason its administration is contraindicated in enteral feeding.

Some drugs also alter absorption by acting on the P-glycoprotein of the enterocytes. This appears to be one of the mechanisms by which grapefruit juice increases the bioavailability of various drugs beyond its inhibitory activity on first pass metabolism.

Based on transport and distribution

Drugs also may affect each other by competing for transport proteins in plasma, such as albumin. In these cases the drug that arrives first binds with the plasma protein, leaving the other drug dissolved in the plasma, modifying its expected concentration. The organism has mechanisms to counteract these situations (by, for example, increasing plasma clearance), and thus they are not usually clinically relevant. They may become relevant if other problems are present, such as issues with drug excretion.

Based on metabolism

Diagram of cytochrome P450 isoenzyme 2C9 with the haem group in the centre of the enzyme.

Many drug interactions are due to alterations in drug metabolism. Further, human drug-metabolizing enzymes are typically activated through the engagement of nuclear receptors. One notable system involved in metabolic drug interactions is the enzyme system comprising the cytochrome P450 oxidases.

CYP450

Cytochrome P450 is a very large family of haemoproteins (hemoproteins) that are characterized by their enzymatic activity and their role in the metabolism of a large number of drugs. Of the various families that are present in humans, the most interesting in this respect are the 1, 2 and 3, and the most important enzymes are CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1 and CYP3A4. The majority of the enzymes are also involved in the metabolism of endogenous substances, such as steroids or sex hormones, which is also important should there be interference with these substances. The function of the enzymes can either be stimulated (enzyme induction) or inhibited (enzyme inhibition).

Through enzymatic inhibition and induction

If a drug is metabolized by a CYP450 enzyme and drug B blocks the activity of these enzymes, it can lead to pharmacokinetic alterations. A. This alteration results in drug A remaining in the bloodstream for an extended duration, and eventually increase in concentration.

In some instances, the inhibition may reduce the therapeutic effect, if instead the metabolites of the drug is responsible for the effect.

Compounds that increase the efficiency of the enzymes, on the other hand, may have the opposite effect and increase the rate of metabolism.

Oligonucleotide therapeutics

Unlike small-molecule drugs, approved RNAi-based therapeutics such as siRNA drugs do not significantly induce or inhibit cytochrome P450 (CYP) enzymes, and therefore require distinct frameworks for evaluating drug–drug interactions (DDIs).

Several interaction mechanisms relevant to oligonucleotide drugs have been identified:

  • Transporter competition: Many GalNAc-conjugated siRNA drugs (e.g., inclisiran, givosiran, lumasiran) are taken up by hepatocytes via the asialoglycoprotein receptor (ASGPR). Co-administration of agents that saturate or alter this receptor pathway may affect hepatic delivery of the siRNA payload.
  • Carrier protein binding: Some oligonucleotide drugs bind serum albumin or lipoproteins. Competition with highly protein-bound small molecules could alter free-drug concentrations.
  • Indirect CYP modulation: siRNA drugs that silence transcription factors controlling CYP gene expression (e.g., silencing of PCSK9 influences LXR/SREBP pathways) may indirectly alter CYP expression in co-treated patients.
  • On-target miRNA network perturbation: Because endogenous microRNA (miRNA) regulates the expression of drug-metabolizing enzymes and transporters, therapeutic oligonucleotides that modulate miRNA activity can produce secondary DDIs via altered enzyme expression rather than direct enzyme binding.


Regulatory guidance from the FDA recommends that sponsors of oligonucleotide drug applications assess DDI potential through in vitro transporter studies and, where indirect CYP modulation is plausible, through induction assays, even when direct CYP inhibition is absent.

Examples of metabolism-based interactions

An example of this is shown in the following table for the CYP1A2 enzyme, showing the substrates (drugs metabolized by this enzyme) and some inductors and inhibitors of its activity:

Drugs related to CYP1A2
SubstratesInhibitorsInductors

Some foods also act as inductors or inhibitors of enzymatic activity. The following table shows the most common:

Foods and their influence on drug metabolism
FoodMechanismDrugs affected
Enzymatic inductor Acenocoumarol, warfarin
Grapefruit juice Enzymatic inhibition
Soya Enzymatic inhibition Clozapine, haloperidol, olanzapine, caffeine, NSAIDs, phenytoin, zafirlukast, warfarin
Garlic Increases antiplatelet activity
Ginseng To be determined Warfarin, heparin, aspirin and NSAIDs
Ginkgo biloba Strong inhibitor of platelet aggregation factor Warfarin, aspirin and NSAIDs
Hypericum perforatum (St John's wort) Enzymatic inductor (CYP450) Warfarin, digoxin, theophylline, cyclosporine, phenytoin and antiretrovirals
Ephedra Receptor level agonist MAOI, central nervous system stimulants, alkaloids ergotamines and xanthines
Kava (Piper methysticum) Unknown Levodopa
Ginger Inhibits thromboxane synthetase (in vitro) Anticoagulants
Chamomile Unknown Benzodiazepines, barbiturates and opioids
Hawthorn Unknown Beta-adrenergic antagonists, cisapride, digoxin, quinidine

Based on excretion

Renal and biliary excretion

Drugs tightly bound to proteins (i.e. not in the free fraction) are not available for renal excretion. Filtration depends on a number of factors including the pH of the urine. Drug interactions may affect those points.

With herbal medicines

Herb-drug interactions are drug interactions that occur between herbal medicines and conventional drugs. These types of interactions may be more common than drug-drug interactions because herbal medicines often contain multiple pharmacologically active ingredients, while conventional drugs typically contain only one. Some such interactions are clinically significant, although most herbal remedies are not associated with drug interactions causing serious consequences. Most catalogued herb-drug interactions are moderate in severity. The most commonly implicated conventional drugs in herb-drug interactions are warfarin, insulin, aspirin, digoxin, and ticlopidine, due to their narrow therapeutic indices. The most commonly implicated herbs involved in such interactions are those containing St. John's Wort, magnesium, calcium, iron, or ginkgo.

Examples

Examples of herb-drug interactions include, but are not limited to:

Mechanisms

The mechanisms underlying most herb-drug interactions are not fully understood. Interactions between herbal medicines and anticancer drugs typically involve enzymes that metabolize cytochrome P450. For example, St. John's Wort has been shown to induce CYP3A4 and P-glycoprotein in vitro and in vivo.

Underlying factors

The factors or conditions that predispose the appearance of interactions include factors such as old age. This is where human physiology changing with age may affect the interaction of drugs. For example, liver metabolism, kidney function, nerve transmission, or the functioning of bone marrow all decrease with age. In addition, in old age, there is a sensory decrease that increases the chances of errors being made in the administration of drugs. The elderly are also more vulnerable to polypharmacy, and the more drugs a patient takes, the higher is the chance of an interaction.

Genetic factors may also affect the enzymes and receptors, thus altering the possibilities of interactions.

Patients with hepatic or renal diseases already may have difficulties metabolizing and excreting drugs, which may exacerbate the effect of interactions.

Some drugs present an intrinsic increased risk for a harmful interaction, including drugs with a narrow therapeutic index, where the difference between the effective dose and the toxic dose is small. The drug digoxin is an example of this type of drug.

Risks are also increased when the drug presents a steep dose-response curve, and small changes in the dosage produce large changes in the drug's concentration in the blood plasma.

Epidemiology

As of 2008, among adults in the United States of America older than 56, 4% were taking medication and/ or supplements that put them at risk of a major drug interaction. Potential drug-drug interactions have increased over time and are more common in the less-educated elderly even after controlling for age, sex, place of residence, and comorbidity.

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