Drug discovery

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In the fields of medicine, biotechnology and pharmacology, drug discovery is the process by which new candidate medications are discovered. [1]

Contents

Historically, drugs were discovered by identifying the active ingredient from traditional remedies or by serendipitous discovery, as with penicillin. More recently, chemical libraries of synthetic small molecules, natural products or extracts were screened in intact cells or whole organisms to identify substances that had a desirable therapeutic effect in a process known as classical pharmacology. After sequencing of the human genome 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. [2]

Modern drug discovery involves the identification of screening hits, [3] medicinal chemistry [4] 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, the process of drug development can continue. If successful, clinical trials are developed. [5]

Modern drug discovery is thus usually a capital-intensive process that involves large investments by pharmaceutical industry corporations as well as national governments (who provide grants and loan guarantees). 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. [6] In 2010, the research and development cost of each new molecular entity was about US$1.8 billion. [7] In the 21st century, basic discovery research is funded primarily by governments and by philanthropic organizations, while late-stage development is funded primarily by pharmaceutical companies or venture capitalists. [8] To be allowed to come to market, drugs must undergo several successful phases of clinical trials, and pass through a new drug approval process, called the New Drug Application in the United States.

Discovering drugs that may be a commercial success, or a public health success, involves a complex interaction between investors, industry, academia, patent laws, regulatory exclusivity, marketing and the need to balance secrecy with communication. [9] Meanwhile, for disorders whose rarity means that no large commercial success or public health effect can be expected, the orphan drug funding process ensures that people who experience those disorders can have some hope of pharmacotherapeutic advances.

History

The idea that the effect of a drug in the human body is mediated by specific interactions of the drug molecule with biological macromolecules, (proteins or nucleic acids in most cases) led scientists to the conclusion that individual chemicals are required for the biological activity of the drug. This made for the beginning of the modern era in pharmacology, as pure chemicals, instead of crude extracts of medicinal plants, became the standard drugs. Examples of drug compounds isolated from crude preparations are morphine, the active agent in opium, and digoxin, a heart stimulant originating from Digitalis lanata . Organic chemistry also led to the synthesis of many of the natural products isolated from biological sources.

Historically, substances, whether crude extracts or purified chemicals, were screened for biological activity without knowledge of the biological target. Only after an active substance was identified was an effort made to identify the target. This approach is known as classical pharmacology, forward pharmacology, [10] or phenotypic drug discovery. [11]

Later, small molecules were synthesized to specifically target a known physiological/pathological pathway, avoiding the mass screening of banks of stored compounds. This led to great success, such as the work of Gertrude Elion and George H. Hitchings on purine metabolism, [12] [13] the work of James Black [14] on beta blockers and cimetidine, and the discovery of statins by Akira Endo. [15] Another champion of the approach of developing chemical analogues of known active substances was Sir David Jack at Allen and Hanbury's, later Glaxo, who pioneered the first inhaled selective beta2-adrenergic agonist for asthma, the first inhaled steroid for asthma, ranitidine as a successor to cimetidine, and supported the development of the triptans. [16]

Gertrude Elion, working mostly with a group of fewer than 50 people on purine analogues, contributed to the discovery of the first anti-viral; the first immunosuppressant (azathioprine) that allowed human organ transplantation; the first drug to induce remission of childhood leukemia; pivotal anti-cancer treatments; an anti-malarial; an anti-bacterial; and a treatment for gout.

Cloning of human proteins made possible the screening of large libraries of compounds against specific targets thought to be linked to specific diseases. This approach is known as reverse pharmacology and is the most frequently used approach today. [17]

In the 2020s, qubit and quantum computing started to be used to reduce the time needed to drug discovery. [18]

Targets

A "target" is produced within the pharmaceutical industry. [8] Generally, the "target" is the naturally existing cellular or molecular structure involved in the pathology of interest where the drug-in-development is meant to act. [8] However, the distinction between a "new" and "established" target can be made without a full understanding of just what a "target" is. This distinction is typically made by pharmaceutical companies engaged in the discovery and development of therapeutics. [8] In an estimate from 2011, 435 human genome products were identified as therapeutic drug targets of FDA-approved drugs. [19]

"Established targets" are those for which there is a good scientific understanding, supported by a lengthy publication history, of both how the target functions in normal physiology and how it is involved in human pathology. [2] This does not imply that the mechanism of action of drugs that are thought to act through a particular established target is fully understood. [2] Rather, "established" relates directly to the amount of background information available on a target, in particular functional information. In general, "new targets" are all those targets that are not "established targets" but which have been or are the subject of drug discovery efforts. The majority of targets selected for drug discovery efforts are proteins, such as G-protein-coupled receptors (GPCRs) and protein kinases. [20]

Screening and design

The process of finding a new drug against a chosen target for a particular disease usually involves high-throughput screening (HTS), wherein large libraries of chemicals are tested for their ability to modify the target. For example, if the target is a novel GPCR, compounds will be screened for their ability to inhibit or stimulate that receptor (see antagonist and agonist): if the target is a protein kinase, the chemicals will be tested for their ability to inhibit that kinase. [21]

Another function of HTS is to show how selective the compounds are for the chosen target, as one wants to find a molecule which will interfere with only the chosen target, but not other, related targets. [21] To this end, other screening runs will be made to see whether the "hits" against the chosen target will interfere with other related targets – this is the process of cross-screening. [21] Cross-screening is useful because the more unrelated targets a compound hits, the more likely that off-target toxicity will occur with that compound once it reaches the clinic. [21]

It is unlikely that a perfect drug candidate will emerge from these early screening runs. One of the first steps is to screen for compounds that are unlikely to be developed into drugs; for example compounds that are hits in almost every assay, classified by medicinal chemists as "pan-assay interference compounds", are removed at this stage, if they were not already removed from the chemical library. [22] [23] [24] It is often observed that several compounds are found to have some degree of activity, and if these compounds share common chemical features, one or more pharmacophores can then be developed. At this point, medicinal chemists will attempt to use structure–activity relationships (SAR) to improve certain features of the lead compound:

This process will require several iterative screening runs, during which, it is hoped, the properties of the new molecular entities will improve, and allow the favoured compounds to go forward to in vitro and in vivo testing for activity in the disease model of choice.

Amongst the physicochemical properties associated with drug absorption include ionization (pKa), and solubility; permeability can be determined by PAMPA and Caco-2. PAMPA is attractive as an early screen due to the low consumption of drug and the low cost compared to tests such as Caco-2, gastrointestinal tract (GIT) and Blood–brain barrier (BBB) with which there is a high correlation.

A range of parameters can be used to assess the quality of a compound, or a series of compounds, as proposed in the Lipinski's Rule of Five. Such parameters include calculated properties such as cLogP to estimate lipophilicity, molecular weight, polar surface area and measured properties, such as potency, in-vitro measurement of enzymatic clearance etc. Some descriptors such as ligand efficiency [25] (LE) and lipophilic efficiency [26] [27] (LiPE) combine such parameters to assess druglikeness.

While HTS is a commonly used method for novel drug discovery, it is not the only method. It is often possible to start from a molecule which already has some of the desired properties. Such a molecule might be extracted from a natural product or even be a drug on the market which could be improved upon (so-called "me too" drugs). Other methods, such as virtual high throughput screening, where screening is done using computer-generated models and attempting to "dock" virtual libraries to a target, are also often used. [21]

Another method for drug discovery is de novo drug design, in which a prediction is made of the sorts of chemicals that might (e.g.) fit into an active site of the target enzyme. For example, virtual screening and computer-aided drug design are often used to identify new chemical moieties that may interact with a target protein. [28] [29] Molecular modelling [30] and molecular dynamics simulations can be used as a guide to improve the potency and properties of new drug leads. [31] [32] [33]

There is also a paradigm shift in the drug discovery community to shift away from HTS, which is expensive and may only cover limited chemical space, to the screening of smaller libraries (maximum a few thousand compounds). These include fragment-based lead discovery (FBDD) [34] [35] [36] [37] and protein-directed dynamic combinatorial chemistry. [38] [39] [40] [41] [42] The ligands in these approaches are usually much smaller, and they bind to the target protein with weaker binding affinity than hits that are identified from HTS. Further modifications through organic synthesis into lead compounds are often required. Such modifications are often guided by protein X-ray crystallography of the protein-fragment complex. [43] [44] [45] The advantages of these approaches are that they allow more efficient screening and the compound library, although small, typically covers a large chemical space when compared to HTS.

Phenotypic screens have also provided new chemical starting points in drug discovery. [46] [47]   A variety of models have been used including yeast, zebrafish, worms, immortalized cell lines, primary cell lines, patient-derived cell lines and whole animal models. These screens are designed to find compounds which reverse a disease phenotype such as death, protein aggregation, mutant protein expression, or cell proliferation as examples in a more holistic cell model or organism. Smaller screening sets are often used for these screens, especially when the models are expensive or time-consuming to run. [48]   In many cases, the exact mechanism of action of hits from these screens is unknown and may require extensive target deconvolution experiments to ascertain. The growth of the field of chemoproteomics has provided numerous strategies to identify drug targets in these cases. [49]

Once a lead compound series has been established with sufficient target potency and selectivity and favourable drug-like properties, one or two compounds will then be proposed for drug development. The best of these is generally called the lead compound, while the other will be designated as the "backup". These decisions are generally supported by computational modelling innovations. [50] [51] [52]

Some of the main computational techniques used in drug development. Computational techniques play an important role in drug development by helping to identify potential drug candidates, predict their properties, and optimize their design. Drug Developing.png
Some of the main computational techniques used in drug development. Computational techniques play an important role in drug development by helping to identify potential drug candidates, predict their properties, and optimize their design.

Nature as source

Traditionally, many drugs and other chemicals with biological activity have been discovered by studying chemicals that organisms create to affect the activity of other organisms for survival. [54]

Despite the rise of combinatorial chemistry as an integral part of lead discovery process, natural products still play a major role as starting material for drug discovery. [55] A 2007 report [56] found that of the 974 small molecule new chemical entities developed between 1981 and 2006, 63% were natural derived or semisynthetic derivatives of natural products. For certain therapy areas, such as antimicrobials, antineoplastics, antihypertensive and anti-inflammatory drugs, the numbers were higher.[ citation needed ]

Natural products may be useful as a source of novel chemical structures for modern techniques of development of antibacterial therapies. [57]

Plant-derived

Many secondary metabolites produced by plants have potential therapeutic medicinal properties. These secondary metabolites contain, bind to, and modify the function of proteins (receptors, enzymes, etc.). Consequently, plant derived natural products have often been used as the starting point for drug discovery. [58] [59] [60] [61] [3]

History

Until the Renaissance, the vast majority of drugs in Western medicine were plant-derived extracts. [62] This has resulted in a pool of information about the potential of plant species as important sources of starting materials for drug discovery. [63] Botanical knowledge about different metabolites and hormones that are produced in different anatomical parts of the plant (e.g. roots, leaves, and flowers) are crucial for correctly identifying bioactive and pharmacological plant properties. [63] [64] Identifying new drugs and getting them approved for market has proved to be a stringent process due to regulations set by national drug regulatory agencies. [65]

Jasmonates

Chemical structure of methyl jasmonate (JA). Methyl jasmonate.svg
Chemical structure of methyl jasmonate (JA).

Jasmonates are important in responses to injury and intracellular signals. They induce apoptosis [66] [67] and protein cascade via proteinase inhibitor, [66] have defense functions, [68] and regulate plant responses to different biotic and abiotic stresses. [68] [69] Jasmonates also have the ability to directly act on mitochondrial membranes by inducing membrane depolarization via release of metabolites. [70]

Jasmonate derivatives (JAD) are also important in wound response and tissue regeneration in plant cells. They have also been identified to have anti-aging effects on human epidermal layer. [71] It is suspected that they interact with proteoglycans (PG) and glycosaminoglycan (GAG) polysaccharides, which are essential extracellular matrix (ECM) components to help remodel the ECM. [72] The discovery of JADs on skin repair has introduced newfound interest in the effects of these plant hormones in therapeutic medicinal application. [71]

Salicylates

Chemical structure of acetylsalicylic acid, more commonly known as Aspirin. Aspirin-skeletal benzene-circle.svg
Chemical structure of acetylsalicylic acid, more commonly known as Aspirin.

Salicylic acid (SA), a phytohormone, was initially derived from willow bark and has since been identified in many species. It is an important player in plant immunity, although its role is still not fully understood by scientists. [73] They are involved in disease and immunity responses in plant and animal tissues. They have salicylic acid binding proteins (SABPs) that have shown to affect multiple animal tissues. [73] The first discovered medicinal properties of the isolated compound was involved in pain and fever management. They also play an active role in the suppression of cell proliferation. [66] They have the ability to induce death in lymphoblastic leukemia and other human cancer cells. [66] One of the most common drugs derived from salicylates is aspirin, also known as acetylsalicylic acid, with anti-inflammatory and anti-pyretic properties. [73] [74]

Animal-derived

Some drugs used in modern medicine have been discovered in animals or are based on compounds found in animals. For example, the anticoagulant drugs, hirudin and its synthetic congener, bivalirudin, are based on saliva chemistry of the leech, Hirudo medicinalis . [75] Used to treat type 2 diabetes, exenatide was developed from saliva compounds of the Gila monster, a venomous lizard. [76]

Microbial metabolites

Microbes compete for living space and nutrients. To survive in these conditions, many microbes have developed abilities to prevent competing species from proliferating. Microbes are the main source of antimicrobial drugs. Streptomyces isolates have been such a valuable source of antibiotics, that they have been called medicinal molds. The classic example of an antibiotic discovered as a defense mechanism against another microbe is penicillin in bacterial cultures contaminated by Penicillium fungi in 1928.[ citation needed ]

Marine invertebrates

Marine environments are potential sources for new bioactive agents. [77] Arabinose nucleosides discovered from marine invertebrates in 1950s, demonstrated for the first time that sugar moieties other than ribose and deoxyribose can yield bioactive nucleoside structures. It took until 2004 when the first marine-derived drug was approved.[ citation needed ][ dubious ] For example, the cone snail toxin ziconotide, also known as Prialt treats severe neuropathic pain. Several other marine-derived agents are now in clinical trials for indications such as cancer, anti-inflammatory use and pain. One class of these agents are bryostatin-like compounds, under investigation as anti-cancer therapy.[ citation needed ]

Chemical diversity

As above mentioned, combinatorial chemistry was a key technology enabling the efficient generation of large screening libraries for the needs of high-throughput screening. However, now, after two decades of combinatorial chemistry, it has been pointed out that despite the increased efficiency in chemical synthesis, no increase in lead or drug candidates has been reached. [56] This has led to analysis of chemical characteristics of combinatorial chemistry products, compared to existing drugs or natural products. The chemoinformatics concept chemical diversity, depicted as distribution of compounds in the chemical space based on their physicochemical characteristics, is often used to describe the difference between the combinatorial chemistry libraries and natural products. The synthetic, combinatorial library compounds seem to cover only a limited and quite uniform chemical space, whereas existing drugs and particularly natural products, exhibit much greater chemical diversity, distributing more evenly to the chemical space. [55] The most prominent differences between natural products and compounds in combinatorial chemistry libraries is the number of chiral centers (much higher in natural compounds), structure rigidity (higher in natural compounds) and number of aromatic moieties (higher in combinatorial chemistry libraries). Other chemical differences between these two groups include the nature of heteroatoms (O and N enriched in natural products, and S and halogen atoms more often present in synthetic compounds), as well as level of non-aromatic unsaturation (higher in natural products). As both structure rigidity and chirality are well-established factors in medicinal chemistry known to enhance compounds specificity and efficacy as a drug, it has been suggested that natural products compare favourably to today's combinatorial chemistry libraries as potential lead molecules.

Screening

Two main approaches exist for the finding of new bioactive chemical entities from natural sources.

The first is sometimes referred to as random collection and screening of material, but the collection is far from random. Biological (often botanical) knowledge is often used to identify families that show promise. This approach is effective because only a small part of the earth's biodiversity has ever been tested for pharmaceutical activity. Also, organisms living in a species-rich environment need to evolve defensive and competitive mechanisms to survive. Those mechanisms might be exploited in the development of beneficial drugs.

A collection of plant, animal and microbial samples from rich ecosystems can potentially give rise to novel biological activities worth exploiting in the drug development process. One example of successful use of this strategy is the screening for antitumor agents by the National Cancer Institute, which started in the 1960s. Paclitaxel was identified from Pacific yew tree Taxus brevifolia . Paclitaxel showed anti-tumour activity by a previously undescribed mechanism (stabilization of microtubules) and is now approved for clinical use for the treatment of lung, breast, and ovarian cancer, as well as for Kaposi's sarcoma. Early in the 21st century, Cabazitaxel (made by Sanofi, a French firm), another relative of taxol has been shown effective against prostate cancer, also because it works by preventing the formation of microtubules, which pull the chromosomes apart in dividing cells (such as cancer cells). Other examples are: 1. Camptotheca (Camptothecin · Topotecan · Irinotecan · Rubitecan · Belotecan); 2. Podophyllum (Etoposide · Teniposide); 3a. Anthracyclines (Aclarubicin · Daunorubicin · Doxorubicin · Epirubicin · Idarubicin · Amrubicin · Pirarubicin · Valrubicin · Zorubicin); 3b. Anthracenediones (Mitoxantrone · Pixantrone).

The second main approach involves ethnobotany, the study of the general use of plants in society, and ethnopharmacology, an area inside ethnobotany, which is focused specifically on medicinal uses.

Artemisinin, an antimalarial agent from sweet wormtree Artemisia annua , used in Chinese medicine since 200BC is one drug used as part of combination therapy for multiresistant Plasmodium falciparum .

Additionally, since machine learning has become more advanced, virtual screening is now an option for drug developers. AI algorithms are being used to perform virtual screening of chemical compounds, which involves predicting the activity of a compound against a specific target. By using machine learning algorithms to analyse large amounts of chemical data, researchers can identify potential new drug candidates that are more likely to be effective against a specific disease. Algorithms, such as Nearest-Neighbour classifiers, RF, extreme learning machines, SVMs, and deep neural networks (DNNs), are used for VS based on synthesis feasibility and can also predict in vivo activity and toxicity. [78]

Structural elucidation

The elucidation of the chemical structure is critical to avoid the re-discovery of a chemical agent that is already known for its structure and chemical activity. Mass spectrometry is a method in which individual compounds are identified based on their mass/charge ratio, after ionization. Chemical compounds exist in nature as mixtures, so the combination of liquid chromatography and mass spectrometry (LC-MS) is often used to separate the individual chemicals. Databases of mass spectra for known compounds are available and can be used to assign a structure to an unknown mass spectrum. Nuclear magnetic resonance spectroscopy is the primary technique for determining chemical structures of natural products. NMR yields information about individual hydrogen and carbon atoms in the structure, allowing detailed reconstruction of the molecule's architecture.

New Drug Application

When a drug is developed with evidence throughout its history of research to show it is safe and effective for the intended use in the United States, the company can file an application – the New Drug Application (NDA) – to have the drug commercialized and available for clinical application. [79] NDA status enables the FDA to examine all submitted data on the drug to reach a decision on whether to approve or not approve the drug candidate based on its safety, specificity of effect, and efficacy of doses. [79]

See also

Related Research Articles

Combinatorial chemistry comprises chemical synthetic methods that make it possible to prepare a large number of compounds in a single process. These compound libraries can be made as mixtures, sets of individual compounds or chemical structures generated by computer software. Combinatorial chemistry can be used for the synthesis of small molecules and for peptides.

<span class="mw-page-title-main">Reagent</span> Substance added to a system to cause a chemical reaction

In chemistry, a reagent or analytical reagent is a substance or compound added to a system to cause a chemical reaction, or test if one occurs. The terms reactant and reagent are often used interchangeably, but reactant specifies a substance consumed in the course of a chemical reaction. Solvents, though involved in the reaction mechanism, are usually not called reactants. Similarly, catalysts are not consumed by the reaction, so they are not reactants. In biochemistry, especially in connection with enzyme-catalyzed reactions, the reactants are commonly called substrates.

In molecular biology and pharmacology, a small molecule or micromolecule is a low molecular weight organic compound that may regulate a biological process, with a size on the order of 1 nm. Many drugs are small molecules; the terms are equivalent in the literature. Larger structures such as nucleic acids and proteins, and many polysaccharides are not small molecules, although their constituent monomers are often considered small molecules. Small molecules may be used as research tools to probe biological function as well as leads in the development of new therapeutic agents. Some can inhibit a specific function of a protein or disrupt protein–protein interactions.

Cheminformatics refers to the use of physical chemistry theory with computer and information science techniques—so called "in silico" techniques—in application to a range of descriptive and prescriptive problems in the field of chemistry, including in its applications to biology and related molecular fields. Such in silico techniques are used, for example, by pharmaceutical companies and in academic settings to aid and inform the process of drug discovery, for instance in the design of well-defined combinatorial libraries of synthetic compounds, or to assist in structure-based drug design. The methods can also be used in chemical and allied industries, and such fields as environmental science and pharmacology, where chemical processes are involved or studied.

<span class="mw-page-title-main">Drug design</span> Invention of new medications based on knowledge of a biological target

Drug design, often referred to as rational drug design or simply rational design, is the inventive process of finding new medications based on the knowledge of a biological target. The drug is most commonly an organic small molecule that activates or inhibits the function of a biomolecule such as a protein, which in turn results in a therapeutic benefit to the patient. In the most basic sense, drug design involves the design of molecules that are complementary in shape and charge to the biomolecular target with which they interact and therefore will bind to it. Drug design frequently but not necessarily relies on computer modeling techniques. This type of modeling is sometimes referred to as computer-aided drug design. Finally, drug design that relies on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design. In addition to small molecules, biopharmaceuticals including peptides and especially therapeutic antibodies are an increasingly important class of drugs and computational methods for improving the affinity, selectivity, and stability of these protein-based therapeutics have also been developed.

<span class="mw-page-title-main">Medicinal chemistry</span> Scientific branch of chemistry

Medicinal or pharmaceutical chemistry is a scientific discipline at the intersection of chemistry and pharmacy involved with designing and developing pharmaceutical drugs. Medicinal chemistry involves the identification, synthesis and development of new chemical entities suitable for therapeutic use. It also includes the study of existing drugs, their biological properties, and their quantitative structure-activity relationships (QSAR).

<span class="mw-page-title-main">Natural product</span> Chemical compound or substance produced by a living organism, found in nature

A natural product is a natural compound or substance produced by a living organism—that is, found in nature. In the broadest sense, natural products include any substance produced by life. Natural products can also be prepared by chemical synthesis and have played a central role in the development of the field of organic chemistry by providing challenging synthetic targets. The term natural product has also been extended for commercial purposes to refer to cosmetics, dietary supplements, and foods produced from natural sources without added artificial ingredients.

<span class="mw-page-title-main">Chemical biology</span> Scientific discipline

Chemical biology is a scientific discipline between the fields of chemistry and biology. The discipline involves the application of chemical techniques, analysis, and often small molecules produced through synthetic chemistry, to the study and manipulation of biological systems. Although often confused with biochemistry, which studies the chemistry of biomolecules and regulation of biochemical pathways within and between cells, chemical biology remains distinct by focusing on the application of chemical tools to address biological questions.

<span class="mw-page-title-main">Stuart Schreiber</span> American chemist

Stuart Schreiber, Ph.D. is the Morris Loeb Research Professor at Harvard University, a co-Founder of the Broad Institute, Howard Hughes Medical Institute Investigator, Emeritus, and a member of the National Academy of Sciences and National Academy of Medicine. His work integrates chemical biology and human biology to advance the science of therapeutics. Key advances include the discovery that small molecules can function as “molecular glues” that promote protein–protein interactions, the co-discovery of mTOR and its role in nutrient-response signaling, the discovery of histone deacetylases and the demonstration that chromatin marks regulate gene expression, the development and application of diversity-oriented synthesis to microbial therapeutics, and the discovery of vulnerabilities of cancer cells linked to genetic, lineage and cell-state features, including ferroptotic vulnerabilities. His notable awards include the Wolf Prize in Chemistry and the Arthur Cope Award. His approach to discovering new therapeutics guided many biotechnology companies that he founded, including Vertex Pharmaceuticals and Ariad Pharmaceuticals. He has founded or co-founded 14 biotechnology companies, which have developed 16 first-in-human approved drugs or advanced clinical candidates.

<span class="mw-page-title-main">Enzyme inhibitor</span> Molecule that blocks enzyme activity

An enzyme inhibitor is a molecule that binds to an enzyme and blocks its activity. Enzymes are proteins that speed up chemical reactions necessary for life, in which substrate molecules are converted into products. An enzyme facilitates a specific chemical reaction by binding the substrate to its active site, a specialized area on the enzyme that accelerates the most difficult step of the reaction.

<span class="mw-page-title-main">Virtual screening</span>

Virtual screening (VS) is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme.

A chemical library or compound library is a collection of stored chemicals usually used ultimately in high-throughput screening or industrial manufacture. The chemical library can consist in simple terms of a series of stored chemicals. Each chemical has associated information stored in some kind of database with information such as the chemical structure, purity, quantity, and physiochemical characteristics of the compound.

Fragment-based lead discovery (FBLD) also known as fragment-based drug discovery (FBDD) is a method used for finding lead compounds as part of the drug discovery process. Fragments are small organic molecules which are small in size and low in molecular weight. It is based on identifying small chemical fragments, which may bind only weakly to the biological target, and then growing them or combining them to produce a lead with a higher affinity. FBLD can be compared with high-throughput screening (HTS). In HTS, libraries with up to millions of compounds, with molecular weights of around 500 Da, are screened, and nanomolar binding affinities are sought. In contrast, in the early phase of FBLD, libraries with a few thousand compounds with molecular weights of around 200 Da may be screened, and millimolar affinities can be considered useful. FBLD is a technique being used in research for discovering novel potent inhibitors. This methodology could help to design multitarget drugs for multiple diseases. The multitarget inhibitor approach is based on designing an inhibitor for the multiple targets. This type of drug design opens up new polypharmacological avenues for discovering innovative and effective therapies. Neurodegenerative diseases like Alzheimer’s (AD) and Parkinson’s, among others, also show rather complex etiopathologies. Multitarget inhibitors are more appropriate for addressing the complexity of AD and may provide new drugs for controlling the multifactorial nature of AD, stopping its progression.

DNA-encoded chemical libraries (DECL) is a technology for the synthesis and screening on an unprecedented scale of collections of small molecule compounds. DECL is used in medicinal chemistry to bridge the fields of combinatorial chemistry and molecular biology. The aim of DECL technology is to accelerate the drug discovery process and in particular early phase discovery activities such as target validation and hit identification.

Phenotypic screening is a type of screening used in biological research and drug discovery to identify substances such as small molecules, peptides, or RNAi that alter the phenotype of a cell or an organism in a desired manner. Phenotypic screening must be followed up with identification and validation, often through the use of chemoproteomics, to identify the mechanisms through which a phenotypic hit works.

Chemical genetics is the investigation of the function of proteins and signal transduction pathways in cells by the screening of chemical libraries of small molecules. Chemical genetics is analogous to classical genetic screen where random mutations are introduced in organisms, the phenotype of these mutants is observed, and finally the specific gene mutation (genotype) that produced that phenotype is identified. In chemical genetics, the phenotype is disturbed not by introduction of mutations, but by exposure to small molecule tool compounds. Phenotypic screening of chemical libraries is used to identify drug targets or to validate those targets in experimental models of disease. Recent applications of this topic have been implicated in signal transduction, which may play a role in discovering new cancer treatments. Chemical genetics can serve as a unifying study between chemistry and biology. The approach was first proposed by Tim Mitchison in 1994 in an opinion piece in the journal Chemistry & Biology entitled "Towards a pharmacological genetics".

<span class="mw-page-title-main">Classical pharmacology</span> Drug discovery by phenotypic screening

In the field of drug discovery, classical pharmacology, also known as forward pharmacology, or phenotypic drug discovery (PDD), relies on phenotypic screening of chemical libraries of synthetic small molecules, natural products or extracts to identify substances that have a desirable therapeutic effect. Using the techniques of medicinal chemistry, the potency, selectivity, and other properties of these screening hits are optimized to produce candidate drugs.

Early twenty-first century pesticide research has focused on developing molecules that combine low use rates and that are more selective, safer, resistance-breaking and cost-effective. Obstacles include increasing pesticide resistance and an increasingly stringent regulatory environment.

Chemoproteomics entails a broad array of techniques used to identify and interrogate protein-small molecule interactions. Chemoproteomics complements phenotypic drug discovery, a paradigm that aims to discover lead compounds on the basis of alleviating a disease phenotype, as opposed to target-based drug discovery, in which lead compounds are designed to interact with predetermined disease-driving biological targets. As phenotypic drug discovery assays do not provide confirmation of a compound's mechanism of action, chemoproteomics provides valuable follow-up strategies to narrow down potential targets and eventually validate a molecule's mechanism of action. Chemoproteomics also attempts to address the inherent challenge of drug promiscuity in small molecule drug discovery by analyzing protein-small molecule interactions on a proteome-wide scale. A major goal of chemoproteomics is to characterize the interactome of drug candidates to gain insight into mechanisms of off-target toxicity and polypharmacology.

<span class="mw-page-title-main">James Inglese</span> American biochemist

James Inglese is an American biochemist, the director of the Assay Development and Screening Technology laboratory at the National Center for Advancing Translational Sciences, a Center within the National Institutes of Health. His specialty is small molecule high throughput screening. Inglese's laboratory develops methods and strategies in molecular pharmacology with drug discovery applications. The work of his research group and collaborators focuses on genetic and infectious disease-associated biology.

References

  1. "The drug development process". US Food and Drug Administration. 4 January 2018. Retrieved 18 December 2019.
  2. 1 2 3 "The drug development process: Step 1: Discover". US Food and Drug Administration. 4 January 2018. Retrieved 18 December 2019.
  3. 1 2 Helleboid S, Haug C, Lamottke K, et al. The Identification of Naturally Occurring Neoruscogenin as a Bioavailable, Potent, and High-Affinity Agonist of the Nuclear Receptor RORα (NR1F1). Journal of Biomolecular Screening. 2014;19(3):399–406. https://doi.org/10.1177/1087057113497095.
  4. Herrmann, A., Roesner, M., Werner, T. et al. Potent inhibition of HIV replication in primary human cells by novel synthetic polyketides inspired by Aureothin. Sci Rep 10, 1326 (2020). https://doi.org/10.1038/s41598-020-57843-9.
  5. "The drug development process: Step 3: Clinical research". US Food and Drug Administration. 4 January 2018. Retrieved 18 December 2019.
  6. Anson D, Ma J, He JQ (1 May 2009). "Identifying Cardiotoxic Compounds". Genetic Engineering & Biotechnology News. TechNote. Vol. 29, no. 9. Mary Ann Liebert. pp. 34–35. ISSN   1935-472X. OCLC   77706455. Archived from the original on 21 September 2012. Retrieved 25 July 2009.
  7. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (March 2010). "How to improve R&D productivity: the pharmaceutical industry's grand challenge". Nature Reviews. Drug Discovery. 9 (3): 203–14. doi: 10.1038/nrd3078 . PMID   20168317. S2CID   1299234.
  8. 1 2 3 4 Current Model for Financing Drug Development: From Concept Through Approval. Institute of Medicine (US), Forum on Drug Discovery, Development, and Translation, National Academies Press, Washington (DC). 2009.
  9. Warren J (April 2011). "Drug discovery: lessons from evolution". British Journal of Clinical Pharmacology. 71 (4): 497–503. doi:10.1111/j.1365-2125.2010.03854.x. PMC   3080636 . PMID   21395642.
  10. Takenaka T (September 2001). "Classical vs reverse pharmacology in drug discovery". BJU International. 88 (Suppl 2): 7–10, discussion 49–50. doi: 10.1111/j.1464-410X.2001.00112.x . PMID   11589663.
  11. Lee JA, Uhlik MT, Moxham CM, Tomandl D, Sall DJ (May 2012). "Modern phenotypic drug discovery is a viable, neoclassic pharma strategy". Journal of Medicinal Chemistry. 55 (10): 4527–38. doi:10.1021/jm201649s. PMID   22409666.
  12. Elion GB (1993). "The quest for a cure". Annual Review of Pharmacology and Toxicology. 33: 1–23. doi:10.1146/annurev.pa.33.040193.000245. PMID   8494337.
  13. Elion GB. "The purine path to chemotherapy. Nobel Lecture 1988".
  14. Black J. "Drugs from emasculated hormones: the principles of synoptic antagonism. Nobel Lecture 1988" . Retrieved 28 February 2014.
  15. Endo A. "The discovery of the statins and their development" . Retrieved 28 February 2014.
  16. Watts G (2012). "Obituary: Sir David Jack". The Lancet. 379 (9811): 116. doi: 10.1016/S0140-6736(12)60053-1 . S2CID   54305535.
  17. Swinney DC, Anthony J (July 2011). "How were new medicines discovered?". Nature Reviews. Drug Discovery. 10 (7): 507–19. doi:10.1038/nrd3480. PMID   21701501. S2CID   19171881.
  18. "Qubit Pharmaceuticals Accelerates Drug Discovery with Hybrid Quantum Computing". HPC Wire. 30 November 2022.
  19. Rask-Andersen M, Almén MS, Schiöth HB (August 2011). "Trends in the exploitation of novel drug targets". Nature Reviews. Drug Discovery. 10 (8): 579–90. doi:10.1038/nrd3478. PMID   21804595. S2CID   3328752.
  20. Jacobson KA (December 2015). "New paradigms in GPCR drug discovery". Biochemical Pharmacology. 98 (4): 541–555. doi:10.1016/j.bcp.2015.08.085. PMC   4967540 . PMID   26265138.
  21. 1 2 3 4 5 Chen, Ya; Kirchmair, Johannes (6 September 2020). "Cheminformatics in natural product‐based drug discovery". Molecular Informatics. 39 (12): 2000171. doi:10.1002/minf.202000171. ISSN   1868-1743. PMC   7757247 . PMID   32725781.
  22. Baker M (9 January 2017). "Deceptive curcumin offers cautionary tale for chemists". Nature. 541 (7636): 144–145. Bibcode:2017Natur.541..144B. doi: 10.1038/541144a . PMID   28079090.
  23. Dahlin JL, Walters MA (July 2014). "The essential roles of chemistry in high-throughput screening triage". Future Medicinal Chemistry . 6 (11): 1265–90. doi:10.4155/fmc.14.60. PMC   4465542 . PMID   25163000.
  24. Baell JB, Holloway GA (April 2010). "New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays". Journal of Medicinal Chemistry. 53 (7): 2719–40. CiteSeerX   10.1.1.394.9155 . doi:10.1021/jm901137j. PMID   20131845.
  25. Hopkins AL, Groom CR, Alex A (May 2004). "Ligand efficiency: a useful metric for lead selection". Drug Discovery Today. 9 (10): 430–1. doi:10.1016/S1359-6446(04)03069-7. PMID   15109945.
  26. Ryckmans T, Edwards MP, Horne VA, Correia AM, Owen DR, Thompson LR, Tran I, Tutt MF, Young T (August 2009). "Rapid assessment of a novel series of selective CB(2) agonists using parallel synthesis protocols: A Lipophilic Efficiency (LipE) analysis". Bioorganic & Medicinal Chemistry Letters. 19 (15): 4406–9. doi:10.1016/j.bmcl.2009.05.062. PMID   19500981.
  27. Leeson PD, Springthorpe B (November 2007). "The influence of drug-like concepts on decision-making in medicinal chemistry". Nature Reviews. Drug Discovery. 6 (11): 881–90. doi:10.1038/nrd2445. PMID   17971784. S2CID   205476574.
  28. Rester U (July 2008). "From virtuality to reality – Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective". Current Opinion in Drug Discovery & Development. 11 (4): 559–68. PMID   18600572.
  29. Rollinger JM, Stuppner H, Langer T (2008). "Virtual screening for the discovery of bioactive natural products". Natural Compounds as Drugs Volume I. Progress in Drug Research. Vol. 65. pp. 211, 213–49. doi:10.1007/978-3-7643-8117-2_6. ISBN   978-3-7643-8098-4. PMC   7124045 . PMID   18084917.
  30. Barcellos GB, Pauli I, Caceres RA, Timmers LF, Dias R, de Azevedo WF (December 2008). "Molecular modeling as a tool for drug discovery". Current Drug Targets. 9 (12): 1084–91. doi:10.2174/138945008786949388. PMID   19128219.
  31. Durrant JD, McCammon JA (October 2011). "Molecular dynamics simulations and drug discovery". BMC Biology. 9: 71. doi: 10.1186/1741-7007-9-71 . PMC   3203851 . PMID   22035460.
  32. Borhani DW, Shaw DE (January 2012). "The future of molecular dynamics simulations in drug discovery". Journal of Computer-Aided Molecular Design. 26 (1): 15–26. Bibcode:2012JCAMD..26...15B. doi:10.1007/s10822-011-9517-y. PMC   3268975 . PMID   22183577.
  33. Ciemny M, Kurcinski M, Kamel K, Kolinski A, Alam N, Schueler-Furman O, Kmiecik S (May 2018). "Protein-peptide docking: opportunities and challenges". Drug Discovery Today. 23 (8): 1530–1537. doi: 10.1016/j.drudis.2018.05.006 . PMID   29733895.
  34. Erlanson DA, McDowell RS, O'Brien T (July 2004). "Fragment-based drug discovery". Journal of Medicinal Chemistry. 47 (14): 3463–82. doi:10.1021/jm040031v. PMID   15214773.
  35. Folkers G, Jahnke W, Erlanson DA, Mannhold R, Kubinyi H (2006). Fragment-based Approaches in Drug Discovery (Methods and Principles in Medicinal Chemistry). Weinheim: Wiley-VCH. ISBN   978-3-527-31291-7.
  36. Erlanson DA (June 2011). "Introduction to fragment-based drug discovery". Fragment-Based Drug Discovery and X-Ray Crystallography. Vol. 317. pp. 1–32. doi:10.1007/128_2011_180. ISBN   978-3-642-27539-5. PMID   21695633.{{cite book}}: |journal= ignored (help)
  37. Edward Z, Michael S (2008). Fragment-based drug discovery a practical approach. Wiley.
  38. Greaney MF, Bhat VT (2010). "Chapter 2: Protein-directed dynamic combinatorial chemistry". In Miller BL (ed.). Dynamic combinatorial chemistry: in drug discovery, bioinorganic chemistry, and materials sciences. New Jersey: John Wiley & Sons. pp. 43–82.
  39. Huang R, Leung IK (July 2016). "Protein-directed dynamic combinatorial chemistry: a guide to protein ligand and inhibitor discovery". Molecules. 21 (7): 910. doi: 10.3390/molecules21070910 . PMC   6273345 . PMID   27438816.
  40. Mondal M, Hirsch AK (April 2015). "Dynamic combinatorial chemistry: a tool to facilitate the identification of inhibitors for protein targets". Chemical Society Reviews. 44 (8): 2455–88. doi: 10.1039/c4cs00493k . PMID   25706945.
  41. Herrmann A (March 2014). "Dynamic combinatorial/covalent chemistry: a tool to read, generate and modulate the bioactivity of compounds and compound mixtures". Chemical Society Reviews. 43 (6): 1899–933. doi:10.1039/c3cs60336a. PMID   24296754.
  42. Hochgürtel M, Lehn JM (2006). "Chapter 16: Dynamic combinatorial diversity in drug discovery". In Jahnke W, Erlanson DA (eds.). Fragment-based approaches in drug discovery . Weinheim: Wiley-VCH. pp.  341–364. ISBN   9783527312917.
  43. Caliandro R, Belviso DB, Aresta BM, de Candia M, Altomare CD (June 2013). "Protein crystallography and fragment-based drug design". Future Medicinal Chemistry. 5 (10): 1121–40. doi:10.4155/fmc.13.84. PMID   23795969.
  44. Chilingaryan Z, Yin Z, Oakley AJ (October 2012). "Fragment-based screening by protein crystallography: successes and pitfalls". International Journal of Molecular Sciences. 13 (10): 12857–79. doi: 10.3390/ijms131012857 . PMC   3497300 . PMID   23202926.
  45. Valade A, Urban D, Beau JM (January–February 2007). "Two galactosyltransferases' selection of different binders from the same uridine-based dynamic combinatorial library". Journal of Combinatorial Chemistry . 9 (1): 1–4. doi:10.1021/cc060033w. PMID   17206823.
  46. Zheng W, Thorne N, McKew JC (November 2013). "Phenotypic screens as a renewed approach for drug discovery". Drug Discovery Today. 18 (21–22): 1067–1073. doi:10.1016/j.drudis.2013.07.001. PMC   4531371 . PMID   23850704.
  47. Swinney DC, Anthony J (June 2011). "How were new medicines discovered?". Nature Reviews. Drug Discovery. 10 (7): 507–519. doi:10.1038/nrd3480. PMID   21701501. S2CID   19171881.
  48. Brown DG, Wobst HJ (March 2020). "Opportunities and Challenges in Phenotypic Screening for Neurodegenerative Disease Research". Journal of Medicinal Chemistry. 63 (5): 1823–1840. doi:10.1021/acs.jmedchem.9b00797. PMID   31268707. S2CID   195798523.
  49. Moellering RE, Cravatt BF (January 2012). "How chemoproteomics can enable drug discovery and development". Chemistry & Biology. 19 (1): 11–22. doi:10.1016/j.chembiol.2012.01.001. PMC   3312051 . PMID   22284350.
  50. Marshall SF, Burghaus R, Cosson V, Cheung SY, Chenel M, DellaPasqua O, et al. (March 2016). "Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation". CPT. 5 (3): 93–122. doi: 10.1002/psp4.12049 . PMC   4809625 . PMID   27069774.
  51. Marshall S, Madabushi R, Manolis E, Krudys K, Staab A, Dykstra K, Visser SA (February 2019). "Model-Informed Drug Discovery and Development: Current Industry Good Practice and Regulatory Expectations and Future Perspectives". CPT. 8 (2): 87–96. doi: 10.1002/psp4.12372 . PMC   6389350 . PMID   30411538.
  52. Van Wijk RC (2020). "Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations". Applied Sciences. 10 (2376): 2376. doi: 10.3390/app10072376 .
  53. Balatti, Galo, E.; Barletta, Patricio, G.; Perez, Andres, D.; Giudicessi, Silvana, L.; Martínez‐Ceron, María, C. (10 October 2022), Inamuddin; Altalhi, Tariq; Cruz, Jorddy N.; Refat, Moamen Salah El‐Deen (eds.), "Machine Learning Approaches to Improve Prediction of Target‐Drug Interactions", Drug Design Using Machine Learning (1 ed.), Wiley, pp. 21–96, doi:10.1002/9781394167258.ch2, ISBN   978-1-394-16628-2 , retrieved 27 January 2023
  54. Reigosa MJ, Pedrol N, González L (2006), Allelopathy: a physiological process with ecological implications, Springer, p. 1, ISBN   978-1-4020-4279-9
  55. 1 2 Feher M, Schmidt JM (2003). "Property distributions: differences between drugs, natural products, and molecules from combinatorial chemistry". Journal of Chemical Information and Computer Sciences. 43 (1): 218–27. doi:10.1021/ci0200467. PMID   12546556.
  56. 1 2 Newman DJ, Cragg GM (March 2007). "Natural products as sources of new drugs over the last 25 years". Journal of Natural Products. 70 (3): 461–77. doi: 10.1021/np068054v . PMID   17309302.
  57. von Nussbaum F, Brands M, Hinzen B, Weigand S, Häbich D (August 2006). "Antibacterial natural products in medicinal chemistry—exodus or revival?". Angewandte Chemie. 45 (31): 5072–129. doi:10.1002/anie.200600350. PMID   16881035. The handling of natural products is cumbersome, requiring nonstandardized workflows and extended timelines. Revisiting natural products with modern chemistry and target-finding tools from biology (reversed genomics) is one option for their revival.
  58. Li JW, Vederas JC (July 2009). "Drug discovery and natural products: end of an era or an endless frontier?". Science. 325 (5937): 161–5. Bibcode:2009Sci...325..161L. doi:10.1126/science.1168243. PMID   19589993. S2CID   207777087. With the current framework of HTS in major pharmaceutical industries and increasing government restrictions on drug approvals, it is possible that the number of new natural product–derived drugs could go to zero. However, this is likely to be temporary, as the potential for new discoveries in the longer term is enormous.
  59. Harvey AL, Edrada-Ebel R, Quinn RJ (2015). "The re-emergence of natural products for drug discovery in the genomics era" (PDF). Nature Reviews. Drug Discovery. 14 (2): 111–29. doi:10.1038/nrd4510. hdl: 10072/141449 . PMID   25614221. S2CID   12369182. Here, we review strategies for natural product screening that harness the recent technical advances that have reduced [technical barriers to screening natural products in high-throughput assays]. The growing appreciation of functional assays and phenotypic screens may further contribute to a revival of interest in natural products for drug discovery.
  60. Newman DJ, Cragg GM (2016). "Natural Products as Sources of New Drugs from 1981 to 2014". Journal of Natural Products. 79 (3): 629–61. doi: 10.1021/acs.jnatprod.5b01055 . PMID   26852623. ... the utilization of natural products and/or their novel structures, in order to discover and develop the final drug entity, is still alive and well. For example, in the area of cancer, over the time frame from around the 1940s to the end of 2014, of the 175 small molecules approved, 131, or 75%, are other than "S" (synthetic), with 85, or 49%, actually being either natural products or directly derived therefrom.
  61. Torre BG, Albericio F (2017). "The Pharmaceutical Industry in 2016. An Analysis of FDA Drug Approvals from a Perspective of the Molecule Type". Molecules (Basel, Switzerland). 22 (3): 368. doi: 10.3390/molecules22030368 . PMC   6155368 . PMID   28264468. The outputs from 2016 indicate the so-called small molecules are losing ground against biologics, biomolecules, and other molecules inspired [by] natural products
  62. Sutton D (2007). "Pedanios Dioscorides: Recording the Medicinal Uses of Plants". In Huxley R (ed.). The Great Naturalists. London: Thames & Hudson, with the Natural History Museum. pp. 32–37. ISBN   978-0-500-25139-3.
  63. 1 2 Ahn K (March 2017). "The worldwide trend of using botanical drugs and strategies for developing global drugs". BMB Reports. 50 (3): 111–116. doi:10.5483/BMBRep.2017.50.3.221. PMC   5422022 . PMID   27998396.
  64. Wink M (September 2015). "Modes of Action of Herbal Medicines and Plant Secondary Metabolites". Medicines. 2 (3): 251–286. doi: 10.3390/medicines2030251 . PMC   5456217 . PMID   28930211.
  65. Oishi S, Kimura SI, Noguchi S, Kondo M, Kondo Y, Shimokawa Y, Iwao Y, Itai S (January 2018). "New scale-down methodology from commercial to lab scale to optimize plant-derived soft gel capsule formulations on a commercial scale". International Journal of Pharmaceutics. 535 (1–2): 371–378. doi:10.1016/j.ijpharm.2017.11.029. PMID   29154803.
  66. 1 2 3 4 Fingrut O, Flescher E (April 2002). "Plant stress hormones suppress the proliferation and induce apoptosis in human cancer cells". Leukemia. 16 (4): 608–16. doi: 10.1038/sj.leu.2402419 . PMID   11960340.
  67. Zhang M, Zhang MW, Zhang L, Zhang L (24 July 2015). "Methyl jasmonate and its potential in cancer therapy". Plant Signaling & Behavior. 10 (9): e1062199. doi:10.1080/15592324.2015.1062199. PMC   4883903 . PMID   26208889.
  68. 1 2 Turner JG, Ellis C, Devoto A (2002). "The jasmonate signal pathway". The Plant Cell. 14 (Suppl): S153–64. doi:10.1105/tpc.000679. PMC   151253 . PMID   12045275.
  69. Ahmad P, Rasool S, Gul A, Sheikh SA, Akram NA, Ashraf M, Kazi AM, Gucel S (2016). "Jasmonates: Multifunctional Roles in Stress Tolerance". Frontiers in Plant Science. 7: 813. doi: 10.3389/fpls.2016.00813 . PMC   4908892 . PMID   27379115.
  70. Rotem R, Heyfets A, Fingrut O, Blickstein D, Shaklai M, Flescher E (March 2005). "Jasmonates: novel anticancer agents acting directly and selectively on human cancer cell mitochondria". Cancer Research. 65 (5): 1984–93. doi: 10.1158/0008-5472.CAN-04-3091 . PMID   15753398.
  71. 1 2 Michelet JF, Olive C, Rieux E, Fagot D, Simonetti L, Galey JB, Dalko-Csiba M, Bernard BA, Pereira R (May 2012). "The anti-ageing potential of a new jasmonic acid derivative (LR2412): in vitro evaluation using reconstructed epidermis Episkin™". Experimental Dermatology. 21 (5): 398–400. doi: 10.1111/j.1600-0625.2012.01480.x . PMID   22509841.
  72. Henriet E, Jäger S, Tran C, Bastien P, Michelet JF, Minondo AM, Formanek F, Dalko-Csiba M, Lortat-Jacob H, Breton L, Vivès RR (September 2017). "A jasmonic acid derivative improves skin healing and induces changes in proteoglycan expression and glycosaminoglycan structure". Biochimica et Biophysica Acta (BBA) - General Subjects. 1861 (9): 2250–2260. doi:10.1016/j.bbagen.2017.06.006. PMID   28602514.
  73. 1 2 3 Klessig DF, Tian M, Choi HW (26 May 2016). "Multiple Targets of Salicylic Acid and Its Derivatives in Plants and Animals". Frontiers in Immunology. 7: 206. doi: 10.3389/fimmu.2016.00206 . PMC   4880560 . PMID   27303403.
  74. Pierpoint WS (1994). "Salicylic Acid and its Derivatives in Plants: Medicines, Metabolites and Messenger Molecules". In Kuntz M (ed.). Advances in Botanical Research. Vol. 20. pp. 163–235. doi:10.1016/S0065-2296(08)60217-7. ISBN   978-0-12-809447-1.
  75. Taylor T, Campbell CT, Kelly B (July 2021). "A Review of Bivalirudin for Pediatric and Adult Mechanical Circulatory Support". American Journal of Cardiovascular Drugs. 21 (4): 395–409. doi:10.1007/s40256-020-00450-w. PMC   7654565 . PMID   33174088.
  76. Shyangdan DS, Royle P, Clar C, Sharma P, Waugh N, Snaith A (October 2011). "Glucagon-like peptide analogues for type 2 diabetes mellitus". The Cochrane Database of Systematic Reviews. 2011 (10): CD006423. doi:10.1002/14651858.cd006423.pub2. PMC   6486297 . PMID   21975753.
  77. Faulkner DJ, Newman DJ, Cragg GM (February 2004). "Investigations of the marine flora and fauna of the Islands of Palau". Natural Product Reports. 21 (1): 50–76. doi:10.1039/b300664f. PMID   15039835.
  78. krishgen2023 (10 March 2023). "Artificial Intelligence in Drug Discovery Research: A Brief Overview". Krishgen Biosystems. Retrieved 16 September 2023.{{cite web}}: CS1 maint: numeric names: authors list (link)
  79. 1 2 "The drug development process. Step 4: FDA drug review". US Food and Drug Administration. 4 January 2018. Retrieved 18 December 2019.

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