Welcome back to our small molecule drug development series! If you’re only joining us now, check out our introductory article to drug discovery. Otherwise let’s dive straight into the world of drug target identification!

So, What Is a Drug Target?

In the world of drug discovery, target can mean something different depending on whom you ask. It may refer to the intended market for the drug (e.g., developing countries), the therapeutic indications (i.e. what condition(s) the drug is expected to treat), the biological pathway or process that the drug modulates, or the molecular recognition site to which it binds.

In this series, which is focused on small molecule drug development, we consider a drug target as the molecular recognition site to which a drug will bind and modulate. This is most often a protein, such as a receptor, an enzyme, a transporter or ion channel, a signaling protein, or in some cases a structural protein e.g., tubulin or actin.

Although exceptions exist, such as DNA alkylating drugs and bone-modifying agents, the vast majority of drugs on the market and many of those undergoing development today target some type of protein expressed in one or more bodily locations, or in the pathogenic organism in the case of anti-infective drugs. The importance of proteins as drug targets is well illustrated by the table below, which lists the targets of the some of the most-prescribed* small molecule drugs in the US, based on a listing from 2017.

Drug Main therapeutic use(s) Target
Paracetamol (acetaminophen) Pain relief, fever reduction COX-2 enzyme (in the brain). Mechanism of action not fully elucidated.
Prednisone Immunosuppressive, anti-inflammatory Glucocorticoid receptor (regulates transcription involved in inflammation, metabolism and development)
Amoxicillin Moderate-spectrum antibiotic DD-transpeptidase (involved in bacterial cell wall biosynthesis)
Gabapentin Seizures and nerve pain α2δ-1, an auxiliary subunit of voltage-gated calcium channels, and other targets. Mechanism of action not fully elucidated.
Lisinopril Blood pressure, heart failure Inhibitor of angiotensin-converting enzymes
Atorvastatin Cholesterol reduction Inhibitor of HMG-CoA reductase (key role in cholesterol production)
Ondansetron To prevent nausea and vomiting caused by cancer treatments and surgery Antagonist of serotonin 5-HT3 receptor

*Drugs are not listed in order of prescription frequency, but all are within the top 10 prescribed drugs in the US (2017 listing).

Finding New Drug Targets

Pharmaceutical pipelines collectively include a broad array of new drug candidates designed to modulate new targets, ‘old’ drugs under investigation for new targets or indications (so-called drug repurposing), and newer and improved versions of marketed drugs with activity against established drug targets. Drugs in the latter category are often referred to as new- or second- and third-generation drugs etc., with plenty of examples within the anti-infective category e.g., the penicillin antibiotics and the triazole antifungals, where newer generations exhibit superiority (such as a broader spectrum of activity, greater potency, improved safety) over older generations, while the drug target remains the same.

While efforts to repurpose or improve existing drugs build upon an abundance of knowledge and may therefore be more cost-effective than starting from scratch, the ability to identify and validate brand new drug targets is generally regarded as the greatest source of innovation within drug discovery, with the potential to treat diseases for which no treatment currently exists. Furthermore, the possibility to advance novel, exclusive and patentable targets is also highly attractive to pharmaceutical companies from a competitive standpoint.

Traditional routes to finding new drug targets involve 2 main approaches:

  1. Studying disease pathophysiology

This strategic approach requires a reasonably good understanding of the genetic pathway that underlies a disease phenotype to reveal biochemical steps that may be exploited therapeutically, from which key molecules may be selected as targets.

One notable example is Donepezil (brand name: Aricept), used in the palliative treatment of Alzheimer’s disease. Donepezil inactivates cholinesterases, thus inhibiting acetylcholine hydrolysis and increasing acetylcholine concentrations at cholinergic synapses. The development of Donepezil followed the general consensus that the major phenotypic manifestations of AD are related to acetylcholine deficiency in several areas of the brain.

  1. Classical pharmacology approaches

Here, therapeutic agents are first discovered empirically based on a desired biological activity e.g., antibacterial action, and efforts go into understanding their mechanism of action retrospectively. The mechanism of action of many naturally derived drugs has been elucidated in this manner, for example, aspirin and its derivatives, benzodiazepines, and certain antipsychotics.

While the traditional strategies have led to the development of numerous blockbuster drugs, and the first is still widely used throughout the pharmaceutical industry, newer approaches often try to speed up the target finding process by taking advantage of modern technologies to select and validate new drug targets, starting with the genome as opposed to the disease.

Scouring the Genome for Drug Targets

Several potential drug targets exist along the path from genotype to phenotype for any given disease. Depending upon the point a drug intervenes in a particular disease process, its activity may involve influencing gene expression levels through modulation of a transcription factor or other regulator, altering the activity of a specific protein(s), or activating compensatory mechanisms.

Regardless of where a target lies in a disease path, it is likely to be a protein, and is therefore represented and searchable in the genome. When searching a genome for potential new drug targets, two main classes of genes are considered:

Disease genes

These are genes that are directly implicated in disease development or susceptibility to disease e.g., through mutation.

While many genetic diseases are attributed to mutations in single genes e.g., single-gene disorders such as cystic fibrosis, sickle cell anemia, and hemochromatosis, an overwhelming number of diseases involve both a genetic (which may involve multiple genes) and an environmental component e.g., certain cancers, diabetes, and many neurodegenerative disorders.

Whole genome sequencing is an invaluable tool in the identification of genes and mutations implicated in single-gene disorders, while genome-wide association studies are generally applied to identify new targets in other disease types. However, despite advances in the technologies to identify disease genes, none of the protein targets identified from single-gene disorders to date have proven to be directly targetable by drugs, while the involvement of multiple genes and environmental aspects greatly complicates the task of finding useful disease gene targets within the majority of other diseases.

There is reason for optimism however, and cancer is likely to be the therapeutic area that stands to benefit the most from disease gene identification, since disease-associated mutations are the main driver behind malignancy and our knowledge of the so-called cancer genome is growing rapidly, as recently reviewed by Hyman, 2017 (1).

Disease-modifying genes

This category includes genes that are not mutated but whose under- or over-expression leads to or contributes to disease, as well as proteins whose altered expression levels contribute to disease despite normal gene expression levels.

This is probably the most important class as far as drug discovery is concerned, since the majority of successful drugs work by changing the activity of a functional protein. Unfortunately, finding new genes in this category is not easy, and it is not yet possible to compile a list of disease-modifying targets using genomic tools alone. Right now, two approaches are widely used to find disease-modifying genes:

Gene expression profiling

This strategy assumes that the manifestation of any disease phenotype involves changes to the expression levels of certain genes in the affected cells and tissues. A comparison of the genes whose expression level is altered between healthy and diseased tissue is therefore likely to reveal genes involved in disease onset and/or progression. These genes, most often identified by comparative RNA sequencing, are likely to be ‘critical path genes’ for the disease in question, and tend to include drug targets worth further investigation.

However, genes not directly related to the disease are also likely to be altered as a consequence of the disease phenotype, and these will also be picked up during a gene expression profiling study. Sorting the critical path genes out from the rest is no mean feat. Furthermore, since mRNA levels don’t always correlate with protein expression levels, results from gene expression profiling should be backed up using antibody-mediated or staining methods before targets are selected for further validation.

In summary, gene expression profiling has the potential to reveal useful drug targets, but should be interpreted with caution and also backed up with additional experiments to assess whether or not proteins encoded by potential target genes are actually altered in disease tissues e.g., time-course analyses of gene expression vs. disease phenotype, and/or histological studies.

Comprehensive gene knockout studies

This approach uses existing knowledge about genes or gene families that might be associated with disease to generate a catalogue of transgenic knockout mice, following by standardized screening for phenotypes spanning a given disease spectrum.

While these approaches are more time-consuming and considerably more expensive than gene expression, they have been very useful in validating the targets of a number of existing drug classes. However, adapting the strategy to find brand new drug targets is not straightforward and success in the long run is largely dependent upon the suitability of the phenotypic tests applied to detect therapeutically relevant phenotypes in the transgenic mice.

You Have a Potential Target, Time to Find Drug Candidates, or….?

Imagine you’re working in the field of Alzheimer’s disease and you’ve identified a potential new target based on a very convincing gene expression profiling study. You ask your manager to release funds for you to start screening compound libraries to find new candidate drugs right away. There is no harm in asking, but without at least some level of target validation, you are likely to be disappointed!

So What Is Target Validation?

Target validation refers to the experimental approaches used to increase the credibility of a potential drug target, and is an absolutely essential step in any drug discovery program. In simple terms, this step involves running assays to determine whether or not drugs that affect a potential target will have the expected effect on cells, tissues, and animal models of the disease. Target validation usually also involves genetic approaches such as gene silencing or knockout to provide proof that the potential target actually plays a role in the disease in question.

That was a short description of target validation, but do stay tuned for the rest of this series over the coming months. Our next topic in the series will be assay development and validation for target validation and compound screening!

References

  1. Hyman DM, Taylor BS, Baselga J. Implementing Genome-Driven Oncology. Cell. 2017;168(4):584-99.

Article by Karen O’Hanlon Cohrt PhD. Contact Karen at karen@tempobioscience.com. 

Karen O’Hanlon Cohrt is a Science Writer with a PhD in biotechnology from Maynooth University, Ireland (2011). After her PhD,  Karen moved to Denmark and held postdoctoral positions in mycology and later in human cell cycle regulation, before moving to the world of drug discovery. Her broad research background provides the technical know-how to support scientists in diverse areas, and this in combination with her passion for writing helps her to keep abreast of exciting research developments as they unfold. Follow Karen on Twitter @KarenOHCohrt. Karen has been a science writer since 2014; you can find her other work on her portfolio.

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