Drug Discovery

From DrugPedia: A Wikipedia for Drug discovery

Current revision

Drug Discovery is a process of identifying the new chemical compound that behaves as a drug like molecule.But this is a very slow process and may take around 7-8 years and large amount of money.For those engaged in drug design the research phase can be broken down into two main tasks: identification of new compounds showing some activity against a target biological receptor, and the progressive optimization of these leads to yield a compound with improved potency and physicochemical properties in vitro, and, eventually, improved efficacy, pharmacokinetics, and toxicological profiles in vivo.

The main aim of the insilico process is to reduce the cost and time involved in discovering a new drug molecule. For example

1. Instead of synthesis of a large number of compound in the laboratory and then study their activity ,use cheminformatics software like Chemdraw ,Hyperchem that will generate the structure of molecule and this reduce the time and money in drug discovery process.
2. Instead of using High throughput screening, use virtual screening methods like Docking.

Image:800 x 600 Stages spiral-en.jpg

[edit] Steps involved in Drug Discovery

We represent here the process of drug discovery and development in a flow diagram ...

TargetDiscovery ------->TargetValidation------>AssayDevelopment------>Screening &Hits to Lead-----> Lead Optimization----> Development------> ClinicalTrials ------->NDA Market

[edit] Target Discovery

1 Disease Mechanism

• The disease mechanism defines the possible cause or causes of a particular disorder, as well as the path or phenotype of the disease. Understanding the disease mechanism directs research and formulates a possible treatment to slow or reverse the disease process. It also predicts a change of the disease pattern and its implications.
• Disease mechanisms can be broadly classified into the following groups

o Defects in distinct genes—genetic disorders o Infection by bacteria, fungi, or viruses o Immune/autoimmune disease o Trauma and acute disease based on injury or organ failure o Multicausal disease

2 Disease Genes

• Disease genes have been identified based on hereditary patterns even before the knowledge of the DNA sequences of the human genome. Following an original founder mutation, these genetically inherited diseases run in families; examples include phenylketonuria, cystic fibrosis, Huntington disease, Fanconi's anemia, and autosomal-dominant familial Alzheimer's (FAD).
• The specific gene defects or mutations that bring about a hereditary disorder have been identified for a number of diseases. Progress in DNA sequencing technology has enabled rapid identification of disease genes through genetic screening. Early intervention is possible for a limited number of hereditary diseases.
• A large fraction of disease, however, is not based on the mutation of a single gene, but rather on a number of genes that together determine a person's risk of developing a particular disease. For example, certain mutations in the BRCA gene family raise the risk for cancer. However, this risk does not always equal 100 percent certainty, and individuals bearing certain BRCA mutations may never develop cancer. Certain allelic variants can increase susceptibility for diseases, such as the ApoE4 allele does for Alzheimer's.
• Environmental factors such as diet, toxic exposures, trauma, stress, and other life experiences are assumed to interact with genetic susceptibility factors to result in disease. Thus, drug targets may include molecular pathways related to environmental factors.

3 Target Type and ‘Drugability’

• Targets for therapeutic intervention can be broadly classified into these categories:

o Receptors o Proteins and enzymes o DNA o RNA and ribosomal targets

• The "drugability" of a given target is defined either by how well a therapeutic, such as small molecule drugs or antibodies, can access the target, or by the efficacy a therapeutic can actually achieve. A long list a parameters influences drugability of a given target; these include cellular location, development of resistance, transport mechanisms such as export pumps, side effects, toxicity, and others.
• Some target classes, for example, the G-protein coupled receptors (GPCRs), have been successfully targeted, and a sizable number of drugs prescribed today hit this particular class. Therefore, the GPCR target type is considered drugable.

4 Functional Genomics

• Functional genomics can be broadly defined as the systematic analysis of gene activity in healthy versus diseased organisms/organs/tissues/cells.
• Specifically, functional genomics employs the large-scale exploration of gene function that includes the analysis of regulatory networks, biochemical pathways, protein-protein interactions, the effects of gene knockouts or gene upregulation or gain-of-function, and the results of functional complementation of knockouts.
• Functional genomics aims to determine disease mechanisms and to identify disease genes and disease markers. It also aims to guide the understanding of signal transduction pathways that either lead to disease or indicate therapeutic strategies for the development of novel therapeutics.
• Functional genomics relies heavily on disease models that are based on the high homology of genes and their function in a variety of organisms ranging from nematodes to mammals.
• Functional genomics employs high-throughput sequencing and high-density arraying of gene expression and activity of gene products. The information content of functional genomics experiments is exceedingly large; it requires sophisticated statistical analysis, which has accelerated the discipline of bioinformatics

[edit] Target Validation

1 Knock-out/Knock-in/Gain-of-function, Transgenic Models

• Transgenic animals where the target gene is knocked out have become an important experimental approach for the determination of the function of targets (genes) in a whole organism.
• Knockouts of genes that are essential in development are usually lethal. Inducible knockouts, i.e., transgenic models where the transgene can be switched on or off at will in the adult animal, can be used to study the function of such essential genes.
• Disease models are transgenic animal models which present a phenotype that bears the hallmarks of a certain disease. These can be combined with knockouts to study the effect of modulating or inhibiting the function of the drug target.
• Knockins or gain-of-function models reactivate gene expression of the target gene, and often ameliorate or even reverse the disease phenotype. Knockins also are used to create disease models.
• Knockins or gain-of-function can also be lethal. For example, switching on or restoring the function of cell cycle genes in post-mitotic cells often leads to cell death. Selective switching-on of genes might present a therapeutic strategy if such restoration of gene function can be engineered in a tissue- or organ-specific fashion.
• Most neurodegenerative disease models have been generated by introducing mutant genes that cause autosomal-dominant forms of the disease in humans. In these models, the mutant gene (such as APP, presenilin, tau, superoxide dismutase-1, expanded huntingtin) is assumed to result in a toxic gain of function, but the actual mechanisms by which the mutations cause disease phenotypes remain debated.

2 Pathways

• The action and interaction of genes and their gene products is complex. Research aimed to define pathways that control and regulate processes in living organisms provides valuable information for drug discovery.
• The knowledge of a pathway allows definition and separate targeting of upstream or downstream targets. Inhibition or modulation of selected targets could lead to the same therapeutic with fewer side effects or better drugability.
• Knowledge of pathways and their relation to each other helps researchers understand side effect profiles.
• Identification of one disease target can lead to a number of alternative drug targets in the same pathway and increase the possibilities for a novel therapeutic. Examples include the drugs acting on the cholesterol synthesis pathway.

3 Clinical Data

• The best validation of a target is clinical efficacy and safety data.
• Second- and third-generation therapeutics often have better efficacy and side effect profiles based on the clinical trials and track record of first-generation drugs.
• Efficacy in clinical trials, i.e., amelioration or reversal of disease in human patients, is the ultimate validation of a target.
• Efficacy in animal disease models does not always predict the outcome in patients. The reliability of disease models for the prediction of human clinical trials varies widely among diseases and needs to be assessed on a case-by-case basis.

4 Antisense DNA/RNA and RNAi

• Antisense DNA/RNA are oligonucleotides or analogs thereof that are complementary to a specific sequence of RNA or DNA. The underlying concept of antisense therapeutics is that the antisense compound binds to the native target to form a double-stranded sequence and thus inhibits its normal function. An antisense drug for viral retinitis has been approved.
• Interfering RNA or RNAi is a gene silencing phenomenon, whereby specific double-stranded RNAs (dsRNAs) trigger the degradation of homologous messenger RNA (mRNA). The specific dsRNAs are processed into small interfering RNA (siRNA), which initiates the cleavage of the homologous mRNA in a complex named the RNA-induced silencing complex (RISC).
• Introduction of either dsRNA or siRNA into cells leads to inhibition of the biological function encoded in the targeted mRNA-the underlying concept of RNAi therapeutics. For example, this approach is being investigated to silence mutant alleles of tau, APP, ataxin, and SOD1.

5 Chemical Knock-out and Chemical Biology

• The prevailing approach for target validation involves the study of the biology of a disease. Knowledge of the disease mechanism and the underlying biological pathways leads to the identification and characterization of drug targets.
• A fundamentally different approach involves using compound collections to screen for phenotypes generated by exposure to molecules; this connects chemical structures to biological effects from the start irrespective of the molecular targets/pathways that are hit.
• This "chemical biology" takes a holistic and random approach to drug discovery. It may complement traditional, deductive approaches.
• In chemical biology, chemical knockouts are a new method whereby the effect of a chemical compound, not a genetic manipulation, knocks out the function of a gene and thus leads to a readable phenotype. Chemical biology and chemical knockouts rely on the creation of diverse chemical libraries of many thousands of compounds.

[edit] Assay Development

1 In vitro/Cell-based

• In-vitro assays monitor a surrogate readout. Examples for such a readout are the catalytic action of an isolated enzyme, the binding of an antibody to a defined antigen, or the growth of an engineered cell line.
• An in-vitro assay system can be designed using only recombinant reagents, reagents that were isolated from lysates, whole crude lysates, or intact cells.
• Cell-based assays range in their complexity from simple cytotoxicity assays or cell growth to reporter gene assays that monitor activation or upregulation of certain genes or their gene products.
• In-vitro functional assays are usually more complex. They combine several molecular components to mimic the function of a biological process, such as activation of a signal transduction pathway. Biological processes that can be monitored in cell-based functional assays include changes in cell morphology, cell migration, or apoptosis are the catalytic action of an isolated enzyme, the binding of an antibody to a defined antigen, or the growth of an engineered cell line.
• In general, in-vitro assays are more robust and cost-effective, and have fewer ethical implications than whole-animal experiments. For these reasons they are usually chosen for high-throughput screening, where tens of thousands of data points are generated in the hunt for novel drug molecules.

2 In vivo/Animal Models

• In-vivo testing involves whole organisms. It assesses both pharmacology and biological efficacy in parallel.
• Animal models have specific characteristics that mimic human diseases. The technologies for the creation of transgenic animals, where certain genes are either deleted, modulated, or added, have progressed tremendously in the last decade. As a consequence, the predictive power of animal models for human disease and pharmacology is improving. Even so, human biology and disease is so complex that for many diseases or pharmacological parameters, the human remains the definitive model. For some disease, e.g., hepatitis C, adequate models still do not exist.
• It is important to note that some experts in the pharmaceutical industry and the U.S. Food and Drug Administration (FDA) believe that inadequate animal models, or the lack of animal models altogether, are a major hurdle in drug discovery and development.
• Pharmaceutical companies have long used model organisms in preclinical efficacy and safety studies. With the emerging knowledge of whole genomes, researchers are now increasingly seeking animal models not only of specific diseases, but also of their underlying particular pathways to broaden assays from pharmacology to include mechanism of action.
• Regarding current animal models of Alzheimer disease, scientists debate whether they adequately model the disease. Amyloid-depositing models, for example, have scant, if any, cell loss. Disease models are usually incomplete models of pathology or mechanism, and their utility in drug screening is limited by the validity of the pathway in human disease pathogenesis.

3 HTS

• High-throughput screening (HTS) aims to rapidly assess the activity of a large number of compounds or extracts on a given target. The term HTS is used when assays are run in a parallel fashion using multi-well assay plates (96-, 384-, 1536-well).
• Assays run in 1536-well plates with minuscule volumes (single-digit microliter to nanoliter scale) are sometimes referred to as ultra high-throughput screening or UHTS.
• Today, HTS/UHTS commonly involves semi-automation or full automation for liquid handling, sample preparation, running of the actual assays, as well as data analysis. HTS laboratories frequently employ robots and the latest detection technologies for assay readouts.
• Assay development for HTS/UHTS faces formidable challenges in terms of reagent stability and cost, environmental robustness (temperature, oxidation, agitation), and statistics (signal-to-noise ratios, Z and Z' quality measures). Therefore, the ultimate design of a HTS/UHTS assay often differs from its respective lower- throughput format.

[edit] Screening & Hits to Leads

1 Compound Libraries

• Compound libraries are the "bread and butter" of screening. There are several sources for compounds:

o Natural products (NPs) from microbes, plants, or animals. NPs are usually tested as crude extracts first, followed by isolation and identification of active compounds. o (Random) collections of discreetly synthesized compounds. o Random libraries exploring "chemical space." o Combinatorial libraries.

• The total number of possible small organic molecules with a molecular mass of less than 500 that populate "chemical space" is estimated to exceed 1060-vastly more than were ever made or indeed will ever be made.
• Given this near -infinite number of theoretical compounds, one can either focus the search around known molecules or pharmacophores with biological activity, or sample the chemical compound universe with a random selection of diverse representatives. Both approaches are used, and complement each other, in today's drug discovery efforts.
• In contrast to the theoretical small-molecule universe, the idea of "privileged" structures has been advanced. Such structures represent a discreet selection of compounds with the highest probability of having biological activity, i.e., of interacting with the universe of biological diversity that has developed on Earth. Likewise, this biological diversity can be viewed as privileged, because all organisms on Earth together do not contain anywhere near the theoretical number for 300 amino acid proteins, 10390.
• An important practical measure for the value of a random library is chemical diversity, which analyzes how similar one compound in the library is to one other.

2 In silico/CADD and SBDD

• Advances in computing power and in structure determination by x-ray crystallography and NMR have made computer-aided drug design (CADD) and structure-based drug design (SBDD) essential tools for drug discovery.
• Elucidation of protein/DNA/RNA structures has been industrialized in recent years, such that structural information about a given drug target, or the binding conformation of a drug, are available to the scientist at earlier stages of drug discovery. HIV protease inhibitor drugs are a prominent success story for SBDD.
• Virtual (in-silico) screening sifts through large numbers of compounds based on a user-defined set of selection criteria. Selection criteria can be as simple as a physical molecular property such as molecular weight or charge, a chemical property such as number of heteroatoms, number of hydrogen-bond acceptors or donors. Selection criteria can be as complex as a three-dimensional description of a binding pocket of the target protein, including chemical functionality and solvation parameters.
• In-silico screening can involve simple filtering based on static selection criteria (i.e., molecular weight). Alternatively, it can involve actual docking of ligands to a target site, which requires computer-intensive algorithms for conformational analysis, as well as binding energies.
• Selection criteria are often combined, either in Boolean fashion or otherwise, to generate complex queries which, for example, describe a SAR established from experimental data. Scoring functions are used to rank compounds that meet selection criteria.
• Initially, in-silico screening was intended to filter out the majority of compounds that have little chance of hitting a target. In this way, one can either reduce the actual number of compounds being screened in a benchtop assay, or enrich a yet-to-be-screened library with compounds that have a chance of hitting the target.
• With increasingly sophisticated algorithms describing the interaction of ligands and receptors, in-silico screening is more commonly being implemented in drug discovery. In-silico screening has been particularly helpful in projects where a wide-ranging SAR around a discreet pharmacophore is known (QSAR), or where high-resolution three-dimensional structural information is available (SBDD).

3 Synthesis and Combinatorial Chemistry

• Screening relies on the availability and chemical synthesis of compounds.
• Today, a chemist typically supplies new compounds to the screener in milligram or even sub-milligram amounts. Compound synthesis often involves the synthesis of precursors, which can serve as the starting point for a compound series. Such precursors tend to involve scale-up procedures, since larger amounts are needed for subsequent analoging.
• By rule of thumb, one chemist synthesizes, purifies, and characterizes about 100 novel compounds per year, fewer if the task is complex. It takes approximately 10,000 different compounds to develop a drug that will make it to market.
• The large capacity and appetite of screening operations has motivated chemists to develop new approaches involving parallel synthesis of many compounds. Such parallel synthesis is called fast analoging when chemical space is explored around a defined pharmacophore, or combinatorial chemistry when compounds are created by combining arrays of building blocks employing the same underlying chemistry. Both technologies have led to large libraries of synthetic compounds that are used for screening.

4 Primary Screen

• A primary screen is designed to rapidly identify hits from compound libraries.
• The goals are to minimize the number of false positives and to maximize the number of confirmed hits. One philosophy often quoted by people in screening operations, especially HTS environments, is not to fret about compounds that were missed but to really care about the quality of data for the compounds that repeat.
• Depending on the assay, hit rates typically range between 0.1 percent and 5 percent. This number also depends on the cutoff parameters set by the researchers, as well as the dynamic range of a given assay.
• Typically, primary screens are initially run in multiplets (i.e., two, three, or more assay data points) of single compound concentrations. Readouts are expressed as percent activity in comparison to a positive (100 percent) and a negative (0 percent) control.
• Hits are then retested a second time (or more often, depending on the assays' robustness). The retest is usually run independently of the first assay, on a different day. If a compound exhibits the same activity within a statistically significant range, it is termed a confirmed hit, which can proceed to dose-response screening.

5 Potency and Dose-response

• Initial potencies of hits are either reported in milligrams per milliliter (mgs/mL), where the molecular weight of compounds is not weighed in, or in micromolar (uM), which takes into account the different molecular weights of compounds.
• Most hits have potencies between 1 and 100 uM, somewhat dependent on the dynamic range and cutoff of assays.
• Hits with potencies in the nanomolar (nM) range are rare.
• Establishing a dose-response relationship is an important step in hit verification. It typically involves a so-called secondary screen. In the secondary screen, a range of compound concentrations usually prepared by serial dilution is tested in an assay to assess the concentration or dose dependence of the assay's readout.
• Typically, this dose-response is expressed as an IC50 in enzyme-, protein-, antibody-, or cell-based assays, or as an EC50 in in-vivo experiments.
• The shape of a dose-response curve, where drug concentration is recorded on the x-axis and drug effect on the y-axis, often provides information about the mechanism of action (MOA).

6 Counterscreens and Selectivity

• Confirmed hits proceed to a series of counterscreens. These assays usually include drug targets of the same protein or receptor family, for example, panels of GPCRs or kinases. In cases where selectivity between subtypes is important, counterscreens might include a panel of homologous enzymes, different protein complexes, or heterooligomers. Counterscreens profile the action of a confirmed hit on a defined spectrum of biological target classes.
• Selectivity toward a drug target decreases the risk of so-called off-target side effects. Selectivity and potency are often coupled, i.e., selectivity increases with better potency.
• Counterscreens are also used to confirm the mechanism of action. For example, if a drug molecule is believed to interfere with a particular amino acid side-chain in a protein, it will not affect a mutant protein where that residue is changed to a different amino acid. If a drug molecule is interacting with target class-specific residues involved in catalysis, it will not affect a different target class.
• The number and stringency of counterscreens can vary widely and depend on the drug target.

7 Mechanism of Action (MOA)