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Sunday, December 16, 2018

Medication

From Wikipedia, the free encyclopedia

Medication
12-08-18-tilidin-retard.jpg
Packages of medication
Synonymsmedication, drug, pharmaceutical, pharmaceutical preparation, pharmaceutical product, medicinal product, medicine, medicament, remedy

A medication (also referred to as medicine, pharmaceutical drug, or simply drug) is a drug used to diagnose, cure, treat, or prevent disease. Drug therapy (pharmacotherapy) is an important part of the medical field and relies on the science of pharmacology for continual advancement and on pharmacy for appropriate management.

Drugs are classified in various ways. One of the key divisions is by level of control, which distinguishes prescription drugs (those that a pharmacist dispenses only on the order of a physician, physician assistant, or qualified nurse) from over-the-counter drugs (those that consumers can order for themselves). Another key distinction is between traditional small-molecule drugs, usually derived from chemical synthesis, and biopharmaceuticals, which include recombinant proteins, vaccines, blood products used therapeutically (such as IVIG), gene therapy, monoclonal antibodies and cell therapy (for instance, stem-cell therapies). Other ways to classify medicines are by mode of action, route of administration, biological system affected, or therapeutic effects. An elaborate and widely used classification system is the Anatomical Therapeutic Chemical Classification System (ATC system). The World Health Organization keeps a list of essential medicines.

Drug discovery and drug development are complex and expensive endeavors undertaken by pharmaceutical companies, academic scientists, and governments. As a result of this complex path from discovery to commercialization, partnering has become a standard practice for advancing drug candidates through development pipelines. Governments generally regulate what drugs can be marketed, how drugs are marketed, and in some jurisdictions, drug pricing. Controversies have arisen over drug pricing and disposal of used drugs.

Definition

In Europe, the term is "medicinal product", and it is defined by EU law as: "(a) Any substance or combination of substances presented as having properties for treating or preventing disease in human beings; or (b) Any substance or combination of substances which may be used in or administered to human beings either with a view to restoring, correcting or modifying physiological functions by exerting a pharmacological, immunological or metabolic action, or to making a medical diagnosis."

In the US, a "drug" is:
  • A substance recognized by an official pharmacopoeia or formulary.
  • A substance intended for use in the diagnosis, cure, mitigation, treatment, or prevention of disease.
  • A substance (other than food) intended to affect the structure or any function of the body.
  • A substance intended for use as a component of a medicine but not a device or a component, part or accessory of a device.
  • Biological products are included within this definition and are generally covered by the same laws and regulations, but differences exist regarding their manufacturing processes (chemical process versus biological process).

Usage

Drug use among elderly Americans has been studied; in a group of 2377 people with average age of 71 surveyed between 2005 and 2006, 84% took at least one prescription drug, 44% took at least one over-the-counter (OTC) drug, and 52% took at least one dietary supplement; in a group of 2245 elderly Americans (average age of 71) surveyed over the period 2010 – 2011, those percentages were 88%, 38%, and 64%.

Classification

One of the key classifications is between traditional small molecule drugs; usually derived from chemical synthesis, and biologic medical products; which include recombinant proteins, vaccines, blood products used therapeutically (such as IVIG), gene therapy, and cell therapy (for instance, stem cell therapies).

Pharmaceuticals or drugs or medicines are classified in various other groups besides their origin on the basis of pharmacological properties like mode of action and their pharmacological action or activity, such as by chemical properties, mode or route of administration, biological system affected, or therapeutic effects. An elaborate and widely used classification system is the Anatomical Therapeutic Chemical Classification System (ATC system). The World Health Organization keeps a list of essential medicines. 

A sampling of classes of medicine includes:
  1. Antipyretics: reducing fever (pyrexia/pyresis)
  2. Analgesics: reducing pain (painkillers)
  3. Antimalarial drugs: treating malaria
  4. Antibiotics: inhibiting germ growth
  5. Antiseptics: prevention of germ growth near burns, cuts and wounds
  6. Mood stabilizers: lithium and valpromide
  7. Hormone replacements: Premarin
  8. Oral contraceptives: Enovid, "biphasic" pill, and "triphasic" pill
  9. Stimulants: methylphenidate, amphetamine
  10. Tranquilizers: meprobamate, chlorpromazine, reserpine, chlordiazepoxide, diazepam, and alprazolam
  11. Statins: lovastatin, pravastatin, and simvastatin
Pharmaceuticals may also be described as "specialty", independent of other classifications, which is an ill-defined class of drugs that might be difficult to administer, require special handling during administration, require patient monitoring during and immediately after administration, have particular regulatory requirements restricting their use, and are generally expensive relative to other drugs.

Types of medicines

For the digestive system

For the cardiovascular system

For the central nervous system

For pain

The main classes of painkillers are NSAIDs, opioids and Local anesthetics.

For consciousness (anesthetic drugs)

Some anesthetics include Benzodiazepines and Barbiturates.

For musculo-skeletal disorders

The main categories of drugs for musculoskeletal disorders are: NSAIDs (including COX-2 selective inhibitors), muscle relaxants, neuromuscular drugs, and anticholinesterases.

For the eye

For the ear, nose and oropharynx

For the respiratory system

For endocrine problems

For the reproductive system or urinary system

For contraception

For obstetrics and gynecology

For the skin

For infections and infestations

For the immune system

For allergic disorders

For nutrition

For neoplastic disorders

For diagnostics

For euthanasia

A euthanaticum is used for euthanasia and physician-assisted suicide. Euthanasia is not permitted by law in many countries, and consequently medicines will not be licensed for this use in those countries.

Administration

February 1918 drawing by Marguerite Martyn of a visiting nurse in St. Louis, Missouri, with medicine and babies

Administration is the process by which a patient takes a medicine. There are three major categories of drug administration; enteral (by mouth), parenteral (into the blood stream), and other (which includes giving a drug through intranasal, topical, inhalation, and rectal means).

It can be performed in various dosage forms such as pills, tablets, or capsules. The drug may contain a single or multiple active ingredients. 

There are many variations in the routes of administration, including intravenous (into the blood through a vein) and oral administration (through the mouth). 

They can be administered all at once as a bolus, at frequent intervals or continuously. Frequencies are often abbreviated from Latin, such as every 8 hours reading Q8H from Quaque VIII Hora.

Drug discovery

In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which new drugs are discovered. 

Historically, drugs were discovered through identifying the active ingredient from traditional remedies or by serendipitous discovery. Later chemical libraries of synthetic small molecules, natural products or extracts were screened in intact cells or whole organisms to identify substances that have a desirable therapeutic effect in a process known as classical pharmacology. Since sequencing of the human genome which allowed rapid cloning and synthesis of large quantities of purified proteins, it has become common practice to use high throughput screening of large compounds libraries against isolated biological targets which are hypothesized to be disease modifying in a process known as reverse pharmacology. Hits from these screens are then tested in cells and then in animals for efficacy. Even more recently, scientists have been able to understand the shape of biological molecules at the atomic level, and to use that knowledge to design (see drug design) drug candidates.

Modern drug discovery involves the identification of screening hits, medicinal chemistry and optimization of those hits to increase the affinity, selectivity (to reduce the potential of side effects), efficacy/potency, metabolic stability (to increase the half-life), and oral bioavailability. Once a compound that fulfills all of these requirements has been identified, it will begin the process of drug development prior to clinical trials. One or more of these steps may, but not necessarily, involve computer-aided drug design. 

Despite advances in technology and understanding of biological systems, drug discovery is still a lengthy, "expensive, difficult, and inefficient process" with low rate of new therapeutic discovery. In 2010, the research and development cost of each new molecular entity (NME) was approximately US$1.8 billion. Drug discovery is done by pharmaceutical companies, with research assistance from universities. The "final product" of drug discovery is a patent on the potential drug. The drug requires very expensive Phase I, II and III clinical trials, and most of them fail. Small companies have a critical role, often then selling the rights to larger companies that have the resources to run the clinical trials.

Development

Drug development is the process of bringing a new drug to the market once a lead compound has been identified through the process of drug discovery. It includes pre-clinical research (microorganisms/animals) and clinical trials (on humans) and may include the step of obtaining regulatory approval to market the drug.

Regulation

The regulation of drugs varies by jurisdiction. In some countries, such as the United States, they are regulated at the national level by a single agency. In other jurisdictions they are regulated at the state level, or at both state and national levels by various bodies, as is the case in Australia. The role of therapeutic goods regulation is designed mainly to protect the health and safety of the population. Regulation is aimed at ensuring the safety, quality, and efficacy of the therapeutic goods which are covered under the scope of the regulation. In most jurisdictions, therapeutic goods must be registered before they are allowed to be marketed. There is usually some degree of restriction of the availability of certain therapeutic goods depending on their risk to consumers.

Depending upon the jurisdiction, drugs may be divided into over-the-counter drugs (OTC) which may be available without special restrictions, and prescription drugs, which must be prescribed by a licensed medical practitioner in accordance with medical guidelines due to the risk of adverse effects and contraindications. The precise distinction between OTC and prescription depends on the legal jurisdiction. A third category, "behind-the-counter" drugs, is implemented in some jurisdictions. These do not require a prescription, but must be kept in the dispensary, not visible to the public, and only be sold by a pharmacist or pharmacy technician. Doctors may also prescribe prescription drugs for off-label use – purposes which the drugs were not originally approved for by the regulatory agency. The Classification of Pharmaco-Therapeutic Referrals helps guide the referral process between pharmacists and doctors. 

The International Narcotics Control Board of the United Nations imposes a world law of prohibition of certain drugs. They publish a lengthy list of chemicals and plants whose trade and consumption (where applicable) is forbidden. OTC drugs are sold without restriction as they are considered safe enough that most people will not hurt themselves accidentally by taking it as instructed. Many countries, such as the United Kingdom have a third category of "pharmacy medicines", which can only be sold in registered pharmacies by or under the supervision of a pharmacist. 

Medical errors include overprescription and polypharmacy, misprescription, contraindication and lack of detail in dosage and administrations instructions. In 2000 the definition of a prescription error was studied using a Delphi method conference; the conference was motivated by ambiguity in the what a prescription error and a need to use a uniform definition in studies.

Drug pricing

United Kingdom

In the UK the Pharmaceutical Price Regulation Scheme is intended to ensure that the National Health Service is able to purchase drugs at reasonable prices. The prices are negotiated between the Department of Health, acting with the authority of Northern Ireland and the UK Government, and the representatives of the Pharmaceutical industry brands, the Association of the British Pharmaceutical Industry (ABPI). For 2017 this payment percentage set by the PPRS will be 4,75%.

Canada

In Canada, the Patented Medicine Prices Review Board examines drug pricing and determines if a price is excessive or not. In these circumstances, drug manufacturers must submit a proposed price to the appropriate regulatory agency. Furthermore, "the International Therapeutic Class Comparison Test is responsible for comparing the National Average Transaction Price of the patented drug product under review" different countries that the prices are being compared to are the following: France, Germany, Italy, Sweden, Switzerland, the United Kingdom, and the United States

Brazil

In Brazil, the prices are regulated through a legislation under the name of Medicamento Genérico (generic drugs) since 1999.

India

In India, drug prices are regulated by the National Pharmaceutical Pricing Authority.

United States

In the United States, drug costs are unregulated, but instead are the result of negotiations between drug companies and insurance companies.

High prices have been attributed to monopolies given to manufacturers by the government and a lack of ability for organizations to negotiate prices.

Blockbuster drug

A blockbuster drug is a drug generating more than $1 billion of revenue for the pharmaceutical company that sells it each year. Cimetidine was the first drug ever to reach more than $1 billion a year in sales, thus making it the first blockbuster drug.
In the pharmaceutical industry, a blockbuster drug is one that achieves acceptance by prescribing physicians as a therapeutic standard for, most commonly, a highly prevalent chronic (rather than acute) condition. Patients often take the medicines for long periods.
Interestingly, despite the enormous advances in science and technology, the number of new blockbuster drugs has halved roughly every 9 years since 1950. This was ascribed to the fact that every new drug competes in effectiveness with every other drugs known so far, other economic factors and ever-tightening regulations.

History

Prescription drug history

Antibiotics first arrived on the medical scene in 1932 thanks to Gerhard Domagk; and were coined the "wonder drugs". The introduction of the sulfa drugs led to the mortality rate from pneumonia in the U.S. to drop from 0.2% each year to 0.05% by 1939. Antibiotics inhibit the growth or the metabolic activities of bacteria and other microorganisms by a chemical substance of microbial origin. Penicillin, introduced a few years later, provided a broader spectrum of activity compared to sulfa drugs and reduced side effects. Streptomycin, found in 1942, proved to be the first drug effective against the cause of tuberculosis and also came to be the best known of a long series of important antibiotics. A second generation of antibiotics was introduced in the 1940s: aureomycin and chloramphenicol. Aureomycin was the best known of the second generation. 

Lithium was discovered in the 19th century for nervous disorders and its possible mood-stabilizing or prophylactic effect; it was cheap and easily produced. As lithium fell out of favor in France, valpromide came into play. This antibiotic was the origin of the drug that eventually created the mood stabilizer category. Valpromide had distinct psychotrophic effects that were of benefit in both the treatment of acute manic states and in the maintenance treatment of manic depression illness. Psychotropics can either be sedative or stimulant; sedatives aim at damping down the extremes of behavior. Stimulants aim at restoring normality by increasing tone. Soon arose the notion of a tranquilizer which was quite different from any sedative or stimulant. The term tranquilizer took over the notions of sedatives and became the dominant term in the West through the 1980s. In Japan, during this time, the term tranquilizer produced the notion of a psyche-stabilizer and the term mood stabilizer vanished.

Premarin (conjugated estrogens, introduced in 1942) and Prempro (a combination estrogen-progestin pill, introduced in 1995) dominated the hormone replacement therapy (HRT) during the 1990s. HRT is not a life-saving drug, nor does it cure any disease. HRT has been prescribed to improve one's quality of life. Doctors prescribe estrogen for their older female patients both to treat short-term menopausal symptoms and to prevent long-term diseases. In the 1960s and early 1970s more and more physicians began to prescribe estrogen for their female patients. between 1991 and 1999, Premarin was listed as the most popular prescription and best-selling drug in America.

The first oral contraceptive, Enovid, was approved by FDA in 1960. Oral contraceptives inhibit ovulation and so prevent conception. Enovid was known to be much more effective than alternatives including the condom and the diaphragm. As early as 1960, oral contraceptives were available in several different strengths by every manufacturer. In the 1980s and 1990s an increasing number of options arose including, most recently, a new delivery system for the oral contraceptive via a transdermal patch. In 1982, a new version of the Pill was introduced, known as the "biphasic" pill. By 1985, a new triphasic pill was approved. Physicians began to think of the Pill as an excellent means of birth control for young women.

Stimulants such as Ritalin (methylphenidate) came to be pervasive tools for behavior management and modification in young children. Ritalin was first marketed in 1955 for narcolepsy; its potential users were middle-aged and the elderly. It wasn't until some time in the 1980s along with hyperactivity in children that Ritalin came onto the market. Medical use of methlyphenidate is predominately for symptoms of attention deficit/hyperactivity disorder (ADHD). Consumption of methylphenidate in the U.S. out-paced all other countries between 1991 and 1999. Significant growth in consumption was also evident in Canada, New Zealand, Australia, and Norway. Currently, 85% of the world's methylphanidate is consumed in America.

The first minor tranquilizer was Meprobamate. Only fourteen months after it was made available, meprobamate had become the country's largest-selling prescription drug. By 1957, meprobamate had become the fastest-growing drug in history. The popularity of meprobamate paved the way for Librium and Valium, two minor tranquilizers that belonged to a new chemical class of drugs called the benzodiazepines. These were drugs that worked chiefly as anti-anxiety agents and muscle relaxants. The first benzodiazepine was Librium. Three months after it was approved, Librium had become the most prescribed tranquilizer in the nation. Three years later, Valium hit the shelves and was ten times more effective as a muscle relaxant and anti-convulsant. Valium was the most versatile of the minor tranquilizers. Later came the widespread adoption of major tranquilizers such as chlorpromazine and the drug reserpine. In 1970 sales began to decline for Valium and Librium, but sales of new and improved tranquilizers, such as Xanax, introduced in 1981 for the newly created diagnosis of panic disorder, soared.

Mevacor (lovastatin) is the first and most influential statin in the American market. The 1991 launch of Pravachol (pravastatin), the second available in the United States, and the release of Zocor (simvastatin) made Mevacor no longer the only statin on the market. In 1998, Viagra was released as a treatment for erectile dysfunction.

Ancient pharmacology

Using plants and plant substances to treat all kinds of diseases and medical conditions is believed to date back to prehistoric medicine. 

The Kahun Gynaecological Papyrus, the oldest known medical text of any kind, dates to about 1800 BC and represents the first documented use of any kind of drug. It and other medical papyri describe Ancient Egyptian medical practices, such as using honey to treat infections and the legs of bee-eaters to treat neck pains. 

Ancient Babylonian medicine demonstrate the use of prescriptions in the first half of the 2nd millennium BC. Medicinal creams and pills were employed as treatments.

On the Indian subcontinent, the Atharvaveda, a sacred text of Hinduism whose core dates from the 2nd millennium BC, although the hymns recorded in it are believed to be older, is the first Indic text dealing with medicine. It describes plant-based drugs to counter diseases. The earliest foundations of ayurveda were built on a synthesis of selected ancient herbal practices, together with a massive addition of theoretical conceptualizations, new nosologies and new therapies dating from about 400 BC onwards. The student of Āyurveda was expected to know ten arts that were indispensable in the preparation and application of his medicines: distillation, operative skills, cooking, horticulture, metallurgy, sugar manufacture, pharmacy, analysis and separation of minerals, compounding of metals, and preparation of alkalis. 

The Hippocratic Oath for physicians, attributed to 5th century BC Greece, refers to the existence of "deadly drugs", and ancient Greek physicians imported drugs from Egypt and elsewhere.

Medieval pharmacology

Al-Kindi's 9th century AD book, De Gradibus and Ibn Sina (Avicenna)'s The Canon of Medicine cover a range of drugs known to Medicine in the medieval Islamic world. 

Medieval medicine saw advances in surgery, but few truly effective drugs existed, beyond opium (found in such extremely popular drugs as the "Great Rest" of the Antidotarium Nicolai at the time) and quinine. Folklore cures and potentially poisonous metal-based compounds were popular treatments. Theodoric Borgognoni, (1205–1296), one of the most significant surgeons of the medieval period, responsible for introducing and promoting important surgical advances including basic antiseptic practice and the use of anaesthetics. Garcia de Orta described some herbal treatments that were used.

Modern pharmacology

For most of the 19th century, drugs were not highly effective, leading Oliver Wendell Holmes, Sr. to famously comment in 1842 that "if all medicines in the world were thrown into the sea, it would be all the better for mankind and all the worse for the fishes".

During the First World War, Alexis Carrel and Henry Dakin developed the Carrel-Dakin method of treating wounds with an irrigation, Dakin's solution, a germicide which helped prevent gangrene.
In the inter-war period, the first anti-bacterial agents such as the sulpha antibiotics were developed. The Second World War saw the introduction of widespread and effective antimicrobial therapy with the development and mass production of penicillin antibiotics, made possible by the pressures of the war and the collaboration of British scientists with the American pharmaceutical industry. 

Medicines commonly used by the late 1920s included aspirin, codeine, and morphine for pain; digitalis, nitroglycerin, and quinine for heart disorders, and insulin for diabetes. Other drugs included antitoxins, a few biological vaccines, and a few synthetic drugs. In the 1930s antibiotics emerged: first sulfa drugs, then penicillin and other antibiotics. Drugs increasingly became "the center of medical practice". In the 1950s other drugs emerged including corticosteroids for inflammation, rauvolfia alkaloids as tranqulizers and antihypertensives, antihistamines for nasal allergies, xanthines for asthma, and typical antipsychotics for psychosis. As of 2007, thousands of approved drugs have been developed. Increasingly, biotechnology is used to discover biopharmaceuticals. Recently, multi-disciplinary approaches have yielded a wealth of new data on the development of novel antibiotics and antibacterials and on the use of biological agents for antibacterial therapy.

In the 1950s new psychiatric drugs, notably the antipsychotic chlorpromazine, were designed in laboratories and slowly came into preferred use. Although often accepted as an advance in some ways, there was some opposition, due to serious adverse effects such as tardive dyskinesia. Patients often opposed psychiatry and refused or stopped taking the drugs when not subject to psychiatric control. 

Governments have been heavily involved in the regulation of drug development and drug sales. In the U.S., the Elixir Sulfanilamide disaster led to the establishment of the Food and Drug Administration, and the 1938 Federal Food, Drug, and Cosmetic Act required manufacturers to file new drugs with the FDA. The 1951 Humphrey-Durham Amendment required certain drugs to be sold by prescription. In 1962 a subsequent amendment required new drugs to be tested for efficacy and safety in clinical trials.

Until the 1970s, drug prices were not a major concern for doctors and patients. As more drugs became prescribed for chronic illnesses, however, costs became burdensome, and by the 1970s nearly every U.S. state required or encouraged the substitution of generic drugs for higher-priced brand names. This also led to the 2006 U.S. law, Medicare Part D, which offers Medicare coverage for drugs.

As of 2008, the United States is the leader in medical research, including pharmaceutical development. U.S. drug prices are among the highest in the world, and drug innovation is correspondingly high. In 2000 U.S.-based firms developed 29 of the 75 top-selling drugs; firms from the second-largest market, Japan, developed eight, and the United Kingdom contributed 10. France, which imposes price controls, developed three. Throughout the 1990s outcomes were similar.

Controversies

Controversies concerning pharmaceutical drugs include patient access to drugs under development and not yet approved, pricing, and environmental issues.

Access to unapproved drugs

Governments worldwide have created provisions for granting access to drugs prior to approval for patients who have exhausted all alternative treatment options and do not match clinical trial entry criteria. Often grouped under the labels of compassionate use, expanded access, or named patient supply, these programs are governed by rules which vary by country defining access criteria, data collection, promotion, and control of drug distribution.

Within the United States, pre-approval demand is generally met through treatment IND (investigational new drug) applications (INDs), or single-patient INDs. These mechanisms, which fall under the label of expanded access programs, provide access to drugs for groups of patients or individuals residing in the US. Outside the US, Named Patient Programs provide controlled, pre-approval access to drugs in response to requests by physicians on behalf of specific, or "named", patients before those medicines are licensed in the patient's home country. Through these programs, patients are able to access drugs in late-stage clinical trials or approved in other countries for a genuine, unmet medical need, before those drugs have been licensed in the patient's home country. 

Patients who have not been able to get access to drugs in development have organized and advocated for greater access. In the United States, ACT UP formed in the 1980s, and eventually formed its Treatment Action Group in part to pressure the US government to put more resources into discovering treatments for AIDS and then to speed release of drugs that were under development.

The Abigail Alliance was established in November 2001 by Frank Burroughs in memory of his daughter, Abigail. The Alliance seeks broader availability of investigational drugs on behalf of terminally ill patients.

In 2013, BioMarin Pharmaceutical was at the center of a high-profile debate regarding expanded access of cancer patients to experimental drugs.

Access to medicines and drug pricing

Essential medicines as defined by the World Health Organization (WHO) are "those drugs that satisfy the health care needs of the majority of the population; they should therefore be available at all times in adequate amounts and in appropriate dosage forms, at a price the community can afford." Recent studies have found that most of the medicines on the WHO essential medicines list, outside of the field of HIV drugs, are not patented in the developing world, and that lack of widespread access to these medicines arise from issues fundamental to economic development – lack of infrastructure and poverty. Médecins Sans Frontières also runs a Campaign for Access to Essential Medicines campaign, which includes advocacy for greater resources to be devoted to currently untreatable diseases that primarily occur in the developing world. The Access to Medicine Index tracks how well pharmaceutical companies make their products available in the developing world. 

World Trade Organization negotiations in the 1990s, including the TRIPS Agreement and the Doha Declaration, have centered on issues at the intersection of international trade in pharmaceuticals and intellectual property rights, with developed world nations seeking strong intellectual property rights to protect investments made to develop new drugs, and developing world nations seeking to promote their generic pharmaceuticals industries and their ability to make medicine available to their people via compulsory licenses. 

Some have raised ethical objections specifically with respect to pharmaceutical patents and the high prices for drugs that they enable their proprietors to charge, which poor people in the developed world, and developing world, cannot afford. Critics also question the rationale that exclusive patent rights and the resulting high prices are required for pharmaceutical companies to recoup the large investments needed for research and development. One study concluded that marketing expenditures for new drugs often doubled the amount that was allocated for research and development. Other critics claim that patent settlements would be costly for consumers, the health care system, and state and federal governments because it would result in delaying access to lower cost generic medicines.

Novartis fought a protracted battle with the government of India over the patenting of its drug, Gleevec, in India, which ended up in India's Supreme Court in a case known as Novartis v. Union of India & Others. The Supreme Court ruled narrowly against Novartis, but opponents of patenting drugs claimed it as a major victory.

Environmental issues

The environmental impact of pharmaceuticals and personal care products is controversial. PPCPs are substances used by individuals for personal health or cosmetic reasons and the products used by agribusiness to boost growth or health of livestock. PPCPs comprise a diverse collection of thousands of chemical substances, including prescription and over-the-counter therapeutic drugs, veterinary drugs, fragrances, and cosmetics. PPCPs have been detected in water bodies throughout the world and ones that persist in the environment are called Environmental Persistent Pharmaceutical Pollutants. The effects of these chemicals on humans and the environment are not yet known, but to date there is no scientific evidence that they affect human health.
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Protein function prediction

From Wikipedia, the free encyclopedia

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.
 
Generally, function can be thought of as, "anything that happens to or through a protein". The Gene Ontology Consortium provides a useful classification of functions, based on a dictionary of well-defined terms divided into three main categories of molecular function, biological process and cellular component. Researchers can query this database with a protein name or accession number to retrieve associated Gene Ontology (GO) terms or annotations based on computational or experimental evidence.

While techniques such as microarray analysis, RNA interference, and the yeast two-hybrid system can be used to experimentally demonstrate the function of a protein, advances in sequencing technologies have made the rate at which proteins can be experimentally characterized much slower than the rate at which new sequences become available. Thus, the annotation of new sequences is mostly by prediction through computational methods, as these types of annotation can often be done quickly and for many genes or proteins at once. The first such methods inferred function based on homologous proteins with known functions (homology-based function prediction). The development of context-based and structure based methods have expanded what information can be predicted, and a combination of methods can now be used to get a picture of complete cellular pathways based on sequence data. The importance and prevalence of computational prediction of gene function is underlined by an analysis of 'evidence codes' used by the GO database: as of 2010, 98% of annotations were listed under the code IEA (inferred from electronic annotation) while only 0.6% were based on experimental evidence.

Function prediction methods

Homology-based methods

A part of a multiple sequence alignment of four different hemoglobin protein sequences. Similar protein sequences, usually indicate shared functions.

Proteins of similar sequence are usually homologous and thus have a similar function. Hence proteins in a newly sequenced genome are routinely annotated using the sequences of similar proteins in related genomes. 

However, closely related proteins do not always share the same function. For example, the yeast Gal1 and Gal3 proteins are paralogs (73% identity and 92% similarity) that have evolved very different functions with Gal1 being a galactokinase and Gal3 being a transcriptional inducer.

There is no hard sequence-similarity threshold for "safe" function prediction; many proteins of barely detectable sequence similarity have the same function while others (such as Gal1 and Gal3) are highly similar but have evolved different functions. As a rule of thumb, sequences that are more than 30-40% identical are usually considered as having the same or a very similar function. 

For enzymes, predictions of specific functions are especially difficult, as they only need a few key residues in their active site, hence very different sequences can have very similar activities. By contrast, even with sequence identity of 70% or greater, 10% of any pair of enzymes have different substrates; and differences in the actual enzymatic reactions are not uncommon near 50% sequence identity.

Sequence motif-based methods

The development of protein domain databases such as Pfam (Protein Families Database) allow us to find known domains within a query sequence, providing evidence for likely functions. The dcGO website contains annotations to both the individual domains and supra-domains (i.e., combinations of two or more successive domains), thus via dcGO Predictor allowing for the function predictions in a more realistic manner. Within protein domains, shorter signatures known as 'motifs' are associated with particular functions, and motif databases such as PROSITE ('database of protein domains, families and functional sites') can be searched using a query sequence. Motifs can, for example, be used to predict subcellular localization of a protein (where in the cell the protein is sent after synthesis). Short signal peptides direct certain proteins to a particular location such as the mitochondria, and various tools exist for the prediction of these signals in a protein sequence. For example, SignalP, which has been updated several times as methods are improved. Thus, aspects of a protein's function can be predicted without comparison to other full-length homologous protein sequences.

Structure-based methods

An alignment of the toxic proteins ricin and abrin. Structural alignments may be used to determine if two proteins have similar functions even when their sequences differ.

Because 3D protein structure is generally more well conserved than protein sequence, structural similarity is a good indicator of similar function in two or more proteins. Many programs have been developed to screen an unknown protein structure against the Protein Data Bank and report similar structures (for example, FATCAT (Flexible structure AlignmenT by Chaining AFPs (Aligned Fragment Pairs) with Twists), CE (combinatorial extension)) and DeepAlign (protein structure alignment beyond spatial proximity). To deal with the situation that many protein sequences have no solved structures, some function prediction servers such as RaptorX are also developed that can first predict the 3D model of a sequence and then use structure-based method to predict functions based upon the predicted 3D model. In many cases instead of the whole protein structure, the 3D structure of a particular motif representing an active site or binding site can be targeted. The Structurally Aligned Local Sites of Activity (SALSA)  method, developed by Mary Jo Ondrechen and students, utilizes computed chemical properties of the individual amino acids to identify local biochemically active sites. Databases such as Catalytic Site Atlas have been developed that can be searched using novel protein sequences to predict specific functional sites.

Genomic context-based methods

Many of the newer methods for protein function prediction are not based on comparison of sequence or structure as above, but on some type of correlation between novel genes/proteins and those that already have annotations. Also known as phylogenomic profiling, these genomic context based methods are based on the observation that two or more proteins with the same pattern of presence or absence in many different genomes most likely have a functional link. Whereas homology-based methods can often be used to identify molecular functions of a protein, context-based approaches can be used to predict cellular function, or the biological process in which a protein acts. For example, proteins involved in the same signal transduction pathway are likely to share a genomic context across all species.

Gene fusion

Gene fusion occurs when two or more genes encode two or more proteins in one organism and have, through evolution, combined to become a single gene in another organism (or vice versa for gene fission). This concept has been used, for example, to search all E. coli protein sequences for homology in other genomes and find over 6000 pairs of sequences with shared homology to single proteins in another genome, indicating potential interaction between each of the pairs. Because the two sequences in each protein pair are non-homologous, these interactions could not be predicted using homology-based methods.

Co-location/co-expression

In prokaryotes, clusters of genes that are physically close together in the genome often conserve together through evolution, and tend to encode proteins that interact or are part of the same operon. Thus, chromosomal proximity also called the gene neighbour method can be used to predict functional similarity between proteins, at least in prokaryotes. Chromosomal proximity has also been seen to apply for some pathways in selected eukaryotic genomes, including Homo sapiens, and with further development gene neighbor methods may be valuable for studying protein interactions in eukaryotes.

Genes involved in similar functions are also often co-transcribed, so that an unannotated protein can often be predicted to have a related function to proteins with which it co-expresses. The guilt by association algorithms developed based on this approach can be used to analyze large amounts of sequence data and identify genes with expression patterns similar to those of known genes. Often, a guilt by association study compares a group of candidate genes (unknown function) to a target group (for example, a group of genes known to be associated with a particular disease), and rank the candidate genes by their likelihood of belonging to the target group based on the data. Based on recent studies, however, it has been suggested that some problems exist with this type of analysis. For example, because many proteins are multifunctional, the genes encoding them may belong to several target groups. It is argued that such genes are more likely to be identified in guilt by association studies, and thus predictions are not specific.

With the accumulation of RNA-seq data that are capable of estimating expression profiles for alternatively spliced isoforms, machine learning algorithms have also been developed for predicting and differentiating functions at the isoform level. This represents an emerging research area in function prediction, which integrates large-scale, heterogeneous genomic data to infer functions at the isoform level.

Computational solvent mapping

Computational solvent mapping of AMA1 protein using fragment-based computational solvent mapping (FTMAP) by computationally scanning the surface of AMA1 with 16 probes (small organic molecules) and defining the locations where the probes cluster (marked as colorful regions on the protein surface)
 
One of the challenges involved in protein function prediction is discovery of the active site. This is complicated by certain active sites not being formed – essentially existing – until the protein undergoes conformational changes brought on by the binding of small molecules. Most protein structures have been determined by X-ray crystallography which requires a purified protein crystal. As a result, existing structural models are generally of a purified protein and as such lack the conformational changes that are created when the protein interacts with small molecules.

Computational solvent mapping utilizes probes (small organic molecules) that are computationally 'moved' over the surface of the protein searching for sites where they tend to cluster. Multiple different probes are generally applied with the goal being to obtain a large number of different protein-probe conformations. The generated clusters are then ranked based on the cluster's average free energy. After computationally mapping multiple probes, the site of the protein where relatively large numbers of clusters form typically corresponds to an active site on the protein.

This technique is a computational adaptation of 'wet lab' work from 1996. It was discovered that ascertaining the structure of a protein while it is suspended in different solvents and then superimposing those structures on one another produces data where the organic solvent molecules (that the proteins were suspended in) typically cluster at the protein's active site. This work was carried out as a response to realizing that water molecules are visible in the electron density maps produced by X-ray crystallography. The water molecules are interacting with the protein and tend to cluster at the protein's polar regions. This led to the idea of immersing the purified protein crystal in other solvents (e.g. ethanol, isopropanol, etc.) to determine where these molecules cluster on the protein. The solvents can be chosen based on what they approximate, that is, what molecule this protein may interact with (e.g. ethanol can probe for interactions with the amino acid serine, isopropanol a probe for threonine, etc.). It is vital that the protein crystal maintains its tertiary structure in each solvent. This process is repeated for multiple solvents and then this data can be used to try to determine potential active sites on the protein. Ten years later this technique was developed into an algorithm by Clodfelter et al.

Network-based methods

An example protein interaction network, produced through the STRING web resource. Patterns of protein interactions within networks are used to infer function. Here, products of the bacterial trp genes coding for tryptophan synthase are shown to interact with themselves and other, related proteins.
 
Guilt by association type algorithms may be used to produce a functional association network for a given target group of genes or proteins. These networks serve as a representation of the evidence for shared/similar function within a group of genes, where nodes represent genes/proteins and are linked to each other by edges representing evidence of shared function.

Integrated networks

Several networks based on different data sources can be combined into a composite network, which can then be used by a prediction algorithm to annotate candidate genes or proteins. For example, the developers of the bioPIXIE system used a wide variety of Saccharomyces cerevisiae (yeast) genomic data to produce a composite functional network for that species. This resource allows the visualization of known networks representing biological processes, as well as the prediction of novel components of those networks. Many algorithms have been developed to predict function based on the integration of several data sources (e.g. genomic, proteomic, protein interaction, etc.), and testing on previously annotated genes indicates a high level of accuracy. Disadvantages of some function prediction algorithms have included a lack of accessibility, and the time required for analysis. Faster, more accurate algorithms such as GeneMANIA (multiple association network integration algorithm) have however been developed in recent years and are publicly available on the web, indicating the future direction of function prediction.

Tools and databases for protein function prediction

STRING: web tool that integrates various data sources for function prediction.

VisANT: Visual analysis of networks and integrative visual data-mining.
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Gene prediction

From Wikipedia, the free encyclopedia

Structure of a eukaryotic gene

In computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced. 

In its earliest days, "gene finding" was based on painstaking experimentation on living cells and organisms. Statistical analysis of the rates of homologous recombination of several different genes could determine their order on a certain chromosome, and information from many such experiments could be combined to create a genetic map specifying the rough location of known genes relative to each other. Today, with comprehensive genome sequence and powerful computational resources at the disposal of the research community, gene finding has been redefined as a largely computational problem. 

Determining that a sequence is functional should be distinguished from determining the function of the gene or its product. Predicting the function of a gene and confirming that the gene prediction is accurate still demands in vivo experimentation through gene knockout and other assays, although frontiers of bioinformatics research are making it increasingly possible to predict the function of a gene based on its sequence alone. 

Gene prediction is one of the key steps in genome annotation, following sequence assembly, the filtering of non-coding regions and repeat masking.

Gene prediction is closely related to the so-called 'target search problem' investigating how DNA-binding proteins (transcription factors) locate specific binding sites within the genome. Many aspects of structural gene prediction are based on current understanding of underlying biochemical processes in the cell such as gene transcription, translation, protein–protein interactions and regulation processes, which are subject of active research in the various omics fields such as transcriptomics, proteomics, metabolomics, and more generally structural and functional genomics.

Empirical methods

In empirical (similarity, homology or evidence-based) gene finding systems, the target genome is searched for sequences that are similar to extrinsic evidence in the form of the known expressed sequence tags, messenger RNA (mRNA), protein products, and homologous or orthologous sequences. Given an mRNA sequence, it is trivial to derive a unique genomic DNA sequence from which it had to have been transcribed. Given a protein sequence, a family of possible coding DNA sequences can be derived by reverse translation of the genetic code. Once candidate DNA sequences have been determined, it is a relatively straightforward algorithmic problem to efficiently search a target genome for matches, complete or partial, and exact or inexact. Given a sequence, local alignment algorithms such as BLAST, FASTA and Smith-Waterman look for regions of similarity between the target sequence and possible candidate matches. Matches can be complete or partial, and exact or inexact. The success of this approach is limited by the contents and accuracy of the sequence database. 

A high degree of similarity to a known messenger RNA or protein product is strong evidence that a region of a target genome is a protein-coding gene. However, to apply this approach systemically requires extensive sequencing of mRNA and protein products. Not only is this expensive, but in complex organisms, only a subset of all genes in the organism's genome are expressed at any given time, meaning that extrinsic evidence for many genes is not readily accessible in any single cell culture. Thus, to collect extrinsic evidence for most or all of the genes in a complex organism requires the study of many hundreds or thousands of cell types, which presents further difficulties. For example, some human genes may be expressed only during development as an embryo or fetus, which might be difficult to study for ethical reasons. 

Despite these difficulties, extensive transcript and protein sequence databases have been generated for human as well as other important model organisms in biology, such as mice and yeast. For example, the RefSeq database contains transcript and protein sequence from many different species, and the Ensembl system comprehensively maps this evidence to human and several other genomes. It is, however, likely that these databases are both incomplete and contain small but significant amounts of erroneous data. 

New high-throughput transcriptome sequencing technologies such as RNA-Seq and ChIP-sequencing open opportunities for incorporating additional extrinsic evidence into gene prediction and validation, and allow structurally rich and more accurate alternative to previous methods of measuring gene expression such as expressed sequence tag or DNA microarray. 

Major challenges involved in gene prediction involve dealing with sequencing errors in raw DNA data, dependence on the quality of the sequence assembly, handling short reads, frameshift mutations, overlapping genes and incomplete genes. 

In prokaryotes it's essential to consider horizontal gene transfer when searching for gene sequence homology. An additional important factor underused in current gene detection tools is existence of gene clusters—operons in both prokaryotes and eukaryotes. Most popular gene detectors treat each gene in isolation, independent of others, which is not biologically accurate.

Ab initio methods

Ab Initio gene prediction is an intrinsic method based on gene content and signal detection. Because of the inherent expense and difficulty in obtaining extrinsic evidence for many genes, it is also necessary to resort to ab initio gene finding, in which the genomic DNA sequence alone is systematically searched for certain tell-tale signs of protein-coding genes. These signs can be broadly categorized as either signals, specific sequences that indicate the presence of a gene nearby, or content, statistical properties of the protein-coding sequence itself. Ab initio gene finding might be more accurately characterized as gene prediction, since extrinsic evidence is generally required to conclusively establish that a putative gene is functional. 

This picture shows how Open Reading Frames (ORFs) can be used for gene prediction. Gene prediction is the process of determining where a coding gene might be in a genomic sequence. Functional proteins must begin with a Start codon (where DNA transcription begins), and end with a Stop codon (where transcription ends). By looking at where those codons might fall in a DNA sequence, one can see where a functional protein might be located. This is important in gene prediction because it can reveal where coding genes are in an entire genomic sequence. In this example, a functional protein can be discovered using ORF3 because it begins with a Start codon, has multiple amino acids, and then ends with a Stop codon, all within the same reading frame.
 
In the genomes of prokaryotes, genes have specific and relatively well-understood promoter sequences (signals), such as the Pribnow box and transcription factor binding sites, which are easy to systematically identify. Also, the sequence coding for a protein occurs as one contiguous open reading frame (ORF), which is typically many hundred or thousands of base pairs long. The statistics of stop codons are such that even finding an open reading frame of this length is a fairly informative sign. (Since 3 of the 64 possible codons in the genetic code are stop codons, one would expect a stop codon approximately every 20–25 codons, or 60–75 base pairs, in a random sequence.) Furthermore, protein-coding DNA has certain periodicities and other statistical properties that are easy to detect in sequence of this length. These characteristics make prokaryotic gene finding relatively straightforward, and well-designed systems are able to achieve high levels of accuracy.

Ab initio gene finding in eukaryotes, especially complex organisms like humans, is considerably more challenging for several reasons. First, the promoter and other regulatory signals in these genomes are more complex and less well-understood than in prokaryotes, making them more difficult to reliably recognize. Two classic examples of signals identified by eukaryotic gene finders are CpG islands and binding sites for a poly(A) tail. 

Second, splicing mechanisms employed by eukaryotic cells mean that a particular protein-coding sequence in the genome is divided into several parts (exons), separated by non-coding sequences (introns). (Splice sites are themselves another signal that eukaryotic gene finders are often designed to identify.) A typical protein-coding gene in humans might be divided into a dozen exons, each less than two hundred base pairs in length, and some as short as twenty to thirty. It is therefore much more difficult to detect periodicities and other known content properties of protein-coding DNA in eukaryotes. 

Advanced gene finders for both prokaryotic and eukaryotic genomes typically use complex probabilistic models, such as hidden Markov models (HMMs) to combine information from a variety of different signal and content measurements. The GLIMMER system is a widely used and highly accurate gene finder for prokaryotes. GeneMark is another popular approach. Eukaryotic ab initio gene finders, by comparison, have achieved only limited success; notable examples are the GENSCAN and geneid programs. The SNAP gene finder is HMM-based like Genscan, and attempts to be more adaptable to different organisms, addressing problems related to using a gene finder on a genome sequence that it was not trained against. A few recent approaches like mSplicer, CONTRAST, or mGene also use machine learning techniques like support vector machines for successful gene prediction. They build a discriminative model using hidden Markov support vector machines or conditional random fields to learn an accurate gene prediction scoring function. 

Ab Initio methods have been benchmarked, with some approaching 100% sensitivity, however as the sensitivity increases, accuracy suffers as a result of increased false positives.

Other signals

Among the derived signals used for prediction are statistics resulting from the sub-sequence statistics like k-mer statistics, Isochore (genetics) or Compositional domain GC composition/uniformity/entropy, sequence and frame length, Intron/Exon/Donor/Acceptor/Promoter and Ribosomal binding site vocabulary, Fractal dimension, Fourier transform of a pseudo-number-coded DNA, Z-curve parameters and certain run features.

It has been suggested that signals other than those directly detectable in sequences may improve gene prediction. For example, the role of secondary structure in the identification of regulatory motifs has been reported. In addition, it has been suggested that RNA secondary structure prediction helps splice site prediction.

Neural networks

Artificial neural networks are computational models that excel at machine learning and pattern recognition. Neural networks must be trained with example data before being able to generalise for experimental data, and tested against benchmark data. Neural networks are able to come up with approximate solutions to problems that are hard to solve by algorithms, provided there is sufficient training data. When applied to gene prediction, neural networks can be used alongside other ab initio methods to predict or identify biological features such as splice sites. One approach involves using a sliding window, which traverses the sequence data in an overlapping manner. The output at each position is a score based on whether the network thinks the window contains a donor splice site or an acceptor splice site. Larger windows offer more accuracy but also require more computational power. A neural network is an example of a signal sensor as its goal is to identify a functional site in the genome.

Combined approaches

Programs such as Maker combine extrinsic and ab initio approaches by mapping protein and EST data to the genome to validate ab initio predictions. Augustus, which may be used as part of the Maker pipeline, can also incorporate hints in the form of EST alignments or protein profiles to increase the accuracy of the gene prediction.

Comparative genomics approaches

As the entire genomes of many different species are sequenced, a promising direction in current research on gene finding is a comparative genomics approach. 

This is based on the principle that the forces of natural selection cause genes and other functional elements to undergo mutation at a slower rate than the rest of the genome, since mutations in functional elements are more likely to negatively impact the organism than mutations elsewhere. Genes can thus be detected by comparing the genomes of related species to detect this evolutionary pressure for conservation. This approach was first applied to the mouse and human genomes, using programs such as SLAM, SGP and TWINSCAN/N-SCAN and CONTRAST.

Multiple informants

TWINSCAN examined only human-mouse synteny to look for orthologous genes. Programs such as N-SCAN and CONTRAST allowed the incorporation of alignments from multiple organisms, or in the case of N-SCAN, a single alternate organism from the target. The use of multiple informants can lead to significant improvements in accuracy.

CONTRAST is composed of two elements. The first is a smaller classifier, identifying donor splice sites and acceptor splice sites as well as start and stop codons. The second element involves constructing a full model using machine learning. Breaking the problem into two means that smaller targeted data sets can be used to train the classifiers, and that classifier can operate independently and be trained with smaller windows. The full model can use the independent classifier, and not have to waste computational time or model complexity re-classifying intron-exon boundaries. The paper in which CONTRAST is introduced proposes that their method (and those of TWINSCAN, etc.) be classified as de novo gene assembly, using alternate genomes, and identifying it as distinct from ab initio, which uses a target 'informant' genomes.

Comparative gene finding can also be used to project high quality annotations from one genome to another. Notable examples include Projector, GeneWise, GeneMapper and GeMoMa. Such techniques now play a central role in the annotation of all genomes.

Pseudogene prediction

Pseudogenes are close relatives of genes, sharing very high sequence homology, but being unable to code for the same protein product. Whilst once relegated as byproducts of gene sequencing, increasingly, as regulatory roles are being uncovered, they are becoming predictive targets in their own right. Pseudogene prediction utilizes existing sequence similarity and ab initio methods, whilst adding additional filtering and methods of identifying pseudogene characteristics.

Sequence similarity methods can be customized for pseudogene prediction using additional filtering to find candidate pseudogenes. This could use disablement detection, which looks for nonsense or frameshift mutations that would truncate or collapse an otherwise functional coding sequence. Additionally, translating DNA into proteins sequences can be more effective than just straight DNA homology.

Content sensors can be filtered according to the differences in statistical properties between pseudogenes and genes, such as a reduced count of CpG islands in pseudogenes, or the differences in G-C content between pseudogenes and their neighbours. Signal sensors also can be honed to pseudogenes, looking for the absence of introns or polyadenine tails. 

Metagenomic gene prediction

Metagenomics is the study of genetic material recovered from the environment, resulting in sequence information from a pool of organisms. Predicting genes is useful for comparative metagenomics. 

Metagenomics tools also fall into the basic categories of using either sequence similarity approaches (MEGAN4) and ab initio techniques (GLIMMER-MG). 

Glimmer-MG is an extension to GLIMMER that relies mostly on an ab initio approach for gene finding and by using training sets from related organisms. The prediction strategy is augmented by classification and clustering gene data sets prior to applying ab initio gene prediction methods. The data is clustered by species. This classification method leverages techniques from metagenomic phylogenetic classification. An example of software for this purpose is, Phymm, which uses interpolated markov models—and PhymmBL, which integrates BLAST into the classification routines. 

MEGAN4 uses a sequence similarity approach, using local alignment against databases of known sequences, but also attempts to classify using additional information on functional roles, biological pathways and enzymes. As in single organism gene prediction, sequence similarity approaches are limited by the size of the database. 

FragGeneScan and MetaGeneAnnotator are popular gene prediction programs based on Hidden Markov model. These predictors account for sequencing errors, partial genes and work for short reads. 

Another fast and accurate tool for gene prediction in metagenomes is MetaGeneMark. This tool is used by the DOE Joint Genome Institute to annotate IMG/M, the largest metagenome collection to date.
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