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Screening Strategies for Drug Discovery: Matching Tools with Solutions

By The Lab Man

(AKA SLAS Director of Education Steve Hamilton)

"The focus should always be on identification of medicines for the patients. Phenotypic approaches and target-based approaches provide different strategies and tools in the drug discovery tool box. It is important to know what tools are available and know how to effectively use the tools, so that the appropriate tool is selected and used in an efficient manner." This is the overarching drug discovery guidance given by David Swinney, co-instructor for the new SLAS Short Course, "Screening Strategies for Drug Discovery: Matching Tools with Solutions."

 

Swinney is currently at the non-profit Institute for Rare and Neglected Diseases Drug Discovery in Mountain View, CA, aka iRND3, working to help discover new medicines for parasitic diseases and rare cancers. His co-instructor, Jonathan Lee, works at Eli Lilly and Company. Most recently Lee has explored the potential of modern phenotypic drug discovery (PDD) in lead generation and drug development and plays an active part in the emerging worldwide PDD community through publications, presentations and hosting the SLAS PDD Special Interest group (active on LinkedIn and at the annual SLAS meeting). Lee also served as a guest editor for a two-part special issue of the Journal of Biomolecular Screening on PDD. Together they present this new short course at SLAS2016 aimed at giving the audience a broader perspective on drug discovery so that they can best utilize their own technologies, have a better understanding of pros and cons associated with a given technology and are able to identify new opportunities.

What You Need to Know

The course addresses:

  • Pharma R&D productivity and its impact. Examples related to clinical failures, lack of efficacy, target validation and proliferation of "me too" drugs.

  • Drug discovery and development process from unmet medical needs to registration, including details of tools and technologies important to each stage.

  • Poor R&D productivity with focus on the potential technical reasons for poor productivity.

  • Drug discovery strategies — first in class and advances in class.

    • The process of target-based drug discovery from target selection to clinical proof of concept (POC) including the pros, cons and screening strategies to improve success with case studies and examples.

    • The process of phenotypic drug discovery from phenotype selection to clinical POC including the pros, cons and screening strategies to improve success with case studies and examples.

  • Considerations for choosing a screening strategy. This includes the differentiation from competing compounds and standards of care, integration of available knowledge, technologies and screening tools as well as consideration of the activities required to move a screening hit forward to clinical POC, including level of mechanistic understanding required, derisking toxicology, clinical doses and timelines.

What's Worked

It is important to gain a broader perspective on the drug discovery process and how it has worked (or not) over the past several decades. "People are surprised that the number of NMEs (new molecular entities) approved per year by the U.S. Food and Drug Administration (FDA) has not significantly changed since the sequencing of the human genome, while the R&D spending has increased greater than 20-fold (adjusted for inflation)," Swinney notes, adding that the first part of the SLAS2016 Short Course covers this important area.

Swinney's comment addresses a 2014 report by the Tufts Center for the Study of Drug Development, "Innovation in the Pharmaceutical Industry: New Estimates of R&D Costs." The following graph based on that study depicts the growth of R&D spending vs. new compounds approved.


Credit: Tufts Center for the Study of Drug Development.

The Tufts report reaches these conclusions for the period from 1963 to 2013:

  • Total capitalized cost per approved new compound grew at an 8.5% compound annual rate; out-of-pocket cost per approved new compound grew at a 9.3% annual rate.

  • Clinical approval success rates declined significantly.

  • Increases in the cash outlays used to conduct clinical development and higher drug failure rates during clinical testing contributed most to the estimated increase in R&D costs.

  • Changes in the time to develop and get new drugs approved and in the cost of capital had modest moderating effects on the increase in total R&D cost.

The Productivity Conundrum

What are the potential reasons for this productivity conundrum? Course instructors examine various drug discovery strategies in the middle portion of the course. One hypothesis is that with the rise of molecular biology, genomics and high-throughput automation tools in the 1990s, the pharma industry eagerly invested in a new discovery and technology paradigm – target-based drug discovery or hypothesis-based drug discovery – well before the technology and knowledge base for that approach was mature enough to be productive. At the same time, they largely abandoned the historically productive approach of observational or phenotypic drug discovery. Indeed, Swinney reports in a 2011 paper (Nature Reviews Drug Discovery 10, 507-519) that between 1999 and 2008, 28 first-in-class drugs approved by the FDA were derived from the phenotypic approach while 17 were derived from a target-based approach.

Both approaches have strengths and weaknesses. Empirical or phenotypic approaches directly test compounds in biologically relevant contexts with minimal assumptions concerning the linkage of specific molecular targets with the therapeutic biology. PDD may identify compounds which require engagement of multiple targets for biological activity, new biological roles for known targets, or novel molecular target classes. With this approach, disease models are critical.

The hypothesis-led or target-based approach can mistakenly focus on targets that ultimately are poorly validated, i.e. linked to a disease. Target-based lead identification has proven to be more difficult and costly than expected and the ability to predict off-target effects is still challenging. In this approach, organism physiology is not included in the picture until late in the process, which can lead to ADME/Tox surprises.

Some feel that the target-based or hypothesis-based approach is just now maturing enough to be productive and drug discovery organizations should employ both methods to ensure a balanced approach. Swinney feels that we are moving toward a new future that integrates hypothesis and empirically driven research. In a 2013 article for the SLAS Electronic Laboratory Neighborhood he said "I tend to think that biology is just too complex and too dynamic at a molecular level to ever predict all the interactions. With phenotypic assays, you are able to identify starting points. Then, once you've identified them, the molecular approach evolves and becomes more relevant."

Understand Full Process; Then Define Roles

Instructors bring all this information together in the final portion of the course to help participants evaluate and choose productive drug discovery and screening strategies. One key is to increase scientist understanding of the entire process from unmet medical need to product registration, and the tools and technologies important to each stage. This reveals their role as part of the whole and allows them to better evaluate their scientific choices in context. "It is very important for those who do screening to understand more about translational biomarkers because these biomarkers should be the endpoints/readouts of the screening assays that will relate screening data to clinical development and the disease in patients," Swinney says. "Translational biomarkers provide the link between the screening assays and patients."

Join Swinney and Lee Jan. 24, 2016 in the San Diego Short Course to learn how to better plan your drug discovery and screening strategies.

About the Author

The Lab Man is SLAS Education Director Steve Hamilton, Ph.D. He is a creative change maker, delivering the fresh thinking and energy that has helped make SLAS the go-to resource for those in life sciences R&D and technology. After years in the drug discovery world, heading many leading-edge automation projects for companies such as Eli Lilly, Scitec and Amgen, Hamilton joined the SLAS professional team in 2010. He received his Ph.D. in analytical chemistry from Purdue University and a B.S. in chemistry from Southeast Missouri State University.

December 21, 2015