Pharma R&D Today
Ideas and Insight supporting all stages of Drug Discovery & Development
Linking Disease Biology to Novel Drug Targets
Posted on June 22nd, 2016 by Jeffrey Paul, PhD in Pharma R&D
Discovering novel drug targets is complicated, and we all know about their low success rate in the clinic. As molecular/genetic tools have become more accessible – and have also pretty much replaced traditional tissue and animal model pharmacology testing – linking the molecular drug target to disease pathways is essential in determining the target’s future success.
Historically, prior to molecular tools, drug discovery required identifying receptors involved in homeostatic and pathophysiologic processes. While characterizing new receptor targets within their physiological systems, research pharmacologic tools such as inhibitors or agonists were then later developed into registered drugs. The successful cardiovascular, respiratory, GI, endocrine, and urologic drugs of the 1960s through the 1990s were basically optimized pharmacologic tools. For instance, I worked on proton pump inhibitors and anti-hypertension agents, which fit well into this paradigm. Some drugs were more specific (i.e., cleaner) and others were more broad-based (i.e., dirtier). Drug development was merely a natural application of basic disease biology and system pharmacology knowledge in patients.
Modern target-based discovery programs typically use cloned genes in simple in vitro response systems. The assays can crank out a plethora of drug targets using high throughput screening without any prior knowledge of their biological relevance. The issue is that without understanding the relationship of the gene or the gene product to the disease and its relevant pathways, there is a high likelihood that the target will not become a successful drug product. So how do we fill in this gap? What is missing? The gap is filled by incorporating disease/systems information into the program. Astra Zeneca authors showed that their discovery targets were more probable to have a positive clinical trial where there was clear drug target linkage to disease.
Initial linking of the target to the disease is done by associating it with genetic studies in patients – however, those associations are not enough. Worth mentioning at this point is the phenotyping drug discovery approach, or the alternative to target-based screening. Some have called this a return to the holistic way because understanding of mechanism is not necessary. Our ancestral cultures have produced some incredible remedies by using a millennium of patient experience through trial and error. Unlike the past, where the village healer used expendable family members in experiments, now sophisticated cellular or tissue response assays can identify a “hit.” Regardless of whether the drug target was identified by genetic or phenotypic approaches, most important is that the drug candidate “grow legs” by understanding its relevance in disease biology systems.
Systems biology and pathway analyses can provide a “reason to believe” that the novel drug target has a good probability to be relevant. This exercise uses a connectivity map and systems information which shows how the novel drug target sits in the universe of biochemical pathways in disease. The map details the pathways and regulatory points of control and may also show the actions of known drugs.
As a clinical pharmacologist, trying my best to understand and predict drug actions in humans, I use these maps to understand how tweaking the drug target may affect various systems. My experience with development of gamma and beta secretase inhibitors for Alzheimer’s Disease is a good example. Using biomarkers for project decision making becomes more clear with disease systems knowledge. Sometimes surprising new uses of drugs (i.e., repurposing) arises from these pathway maps. Cimetidine as a potential anti-cancer therapy is an example. In the end, the bioinformatics and pathway maps should be shared with a molecular disease expert in the therapeutic areas – there are only a few in each field who can provide meaningful feedback to drug discovery.
As a systems person, I am a strong believer in using translational tools, including in vivo animal models to further increase positive predictivity. The most useful are those translational models that demonstrate that the drug target is active and relevant in various models and species. A rich preclinical package that includes translational information is precious to clinical pharmacologists who typically introduce the molecule to humans. Translational approaches employ PK/PD, response biomarkers, and quantitative systems pharmacology to help with sharpening human predictions. “Bench to bedside” is very attractive, referring to a translational exercise of identifying a molecular target and swiftly moving it to patients. Moving a molecular target to the clinic without first linking it to disease biology is a crapshoot at best.
Monogenetic inherited diseases, such as Huntington’s Disease, nicely demonstrates a good target linking to disease and use of bench to bedside approach. An example where I have experience and hope, neuropathic pain, is the Nav1.7 sodium ion channel as a drug target. This channel is mapped within the CNS system and transgenic animal models have been constructed. Remarkably, the transgenic animal phenotypes show much similarity to patients with primary erythromelagia and congenital analgesia, consistent with pathway map predictions. Several drug candidates are now in clinical trials awaiting full target validation. It’s possible that there are subpopulations of fibromyalgia or neuropathic pain populations with overactive Nav1.7 ion channels which could be treated with this drug.
In summary, identifying disease to drug target linkages using pathway analyses and mapping, combined with integration of translational models, will result in more successful drug targets and positive efficacy clinical studies.
 David Cook, Dearg Brown, Robert Alexander, Ruth March, Paul Morgan, Gemma Satterthwaite and Menelas N. Pangalos. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nature Reviews Drug Discovery, Volume 13, June 2014.
 Ellen L. Berg. Systems biology in drug discovery and development. Drug Discovery Today, Volume 19, Number 2. February 2014.
 Paul McGonigle; Bruce Ruggeri. Animal models of human disease: Challenges in enabling translation.; Biochemical Pharmacology, 87 (2014) 162–171.
All opinions shared in this post are the author’s own.
R&D Solutions for Pharma & Life SciencesWe're happy to discuss your needs and show you how Elsevier's Solution can help.
Jeffrey Paul, PhD
Principal at JPharm Consulting
- Emulating start-up success with purpose-built data tools
- Finding Answers Where You Least Expect Them
- Machine Learning Could Make Drug Discovery Faster, Cheaper, Better
- AI Can Work Wonders, But Can It Create Miracle Cures?
- Helping Startups in the Pursuit of Drug Discovery Success