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Applying Pharmacology to New Drug Discovery

The System-independent quantification of molecular drug properties for prediction of therapeutic utility

April 22, 2016 | 9:00 am - 5:00 pm

This course will describe pharmacological principles and procedures to quantify affinity, efficacy, biased signaling and allostery to better screen for new drugs and characterize drug candidates in lead optimization assays.

1. Assay Formats/Experimental Design
     a. Binding
     b. Functional Assays
     c. Null Method Assays

2. Agonism
a. Agonist Affinity/Efficacy
b. Black/Leff Operational model

3. Biased Signaling (Agonism)
     a. Mechanism of Biased Signaling
     b. Quantifying Biased Agonism
     c. Therapeutic application(s)

4. Orthosteric Antagonism (I)
a. Competitive
b. Non-Competitive/Irreversible

5. Orthosteric Antagonism (II)
a. Partial Agonism
b. Inverse Agonism

6. Allosteric Modulation (I)
a. Functional Allosteric Model
b. Negative Allosteric Modulators (NAMs)

7. Allosteric Modulation (II)
a. Positive Allosteric Modulators (PAMs)
b. Allosteric Agonism

8. Drug-Receptor Kinetics
     a. Measuring Target Coverage
     b. Allosteric Proof-of-Concept
     c. Application of Real-Time Kinetics

9. Drug Screening
a. Design of Screening Assays
b. Screening for Allosteric Modulators

Over the past 6 six years, the primary cause of new drug candidate failures (50%) has been failure of therapeutic efficacy. Put another way, drug discovery programs do everything right, get the defined candidate molecule, only to have it fail in therapeutic trials. Among the most prevalent reasons proposed for this shortcoming is the lack of translation of in vitro and recombinant drug activity to therapeutic in vivo whole systems. Drug activity in complete systems can be characterized with the application of pharmacological principles which translate drug behaviors in various organs with molecular scales of affinity and efficacy.

Pharmacological techniques are unique in that they can convert descriptive data (what we see, potency, activity in a given system) to predictive data (molecular scales of activity that can be used to predict activity in all systems including the therapeutic one, i.e. affinity, efficacy). The predicted outcome of this process is a far lower failure rate as molecules are progressed toward clinical testing.

Terry Kenakin Instructor: Terry Kenakin presently is a Professor of Pharmacology in the Department of Pharmacology, University of North Carolina School of Medicine. The course is taught from the perspective of industrial drug discovery; Dr. Kenakin has worked in drug industry for 32 years (7 at Burroughs-Wellcome, RTP, NC and 25 at GlaxoSmithKline, RTP. NC). He is Editor-in-Chief of the Journal of Receptors and Signal Transduction and Co-Editor-in-Chief of Current Opinion in Pharmacology and is on numerous journal Editorial Boards. In addition, he has authored over 200 peer reviewed papers and reviews and has written 10 books on Pharmacology.

Course Material: Summary sheets, exercises with answers, relevant papers are included as well as a pdf of all slides. The course is based on the book A Pharmacology Primer: Techniques for More Effective and Strategic Drug Discovery. 4th Edition, Elsevier/Academic Press, 2014.

April 14

April 15-16

April 16-17

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