How modeling and simulation have enhanced decision making in new drug development.
The idea of model-based drug development championed by Lewis Sheiner, in which pharmacostatistical models of drug efficacy and safety are developed from preclinical and available clinical data, offers a quantitative approach to improving drug development and development decision-making. Examples are presented that support this paradigm. The first example describes a preclinical model of behavioral activity to predict potency and time-course of response in humans and assess the potential for differentiation between compounds. This example illustrates how modeling procedures expounded by Lewis Sheiner provided the means to differentiate potency and the lag time between drug exposure and response and allow for rapid decision making and dose selection. The second example involves planning a Phase 2a dose-ranging and proof of concept trial in Alzheimer's disease (AD). The issue was how to proceed with the study and what criteria to use for a go/no go decision. The combined knowledge of AD disease progression, and preclinical and clinical information about the drug were used to simulate various clinical trial scenarios to identify an efficient and effective Phase 2 study. A design was selected and carried out resulting in a number of important learning experiences as well as extensive financial savings. The motivation for this case in point was the "Learn-Confirm" paradigm described by Lewis Sheiner. The final example describes the use of Pharmacokinetic and Pharmacodynamic (PK/PD) modeling and simulation to confirm efficacy across doses. In the New Drug Application for gabapentin, data from two adequate and well-controlled clinical trials was submitted to the Food and Drug Administration (FDA) in support of the approval of the indication for the treatment of post-herpetic neuralgia. The clinical trial data was not replicated for each of the sought dose levels in the drug application presenting a regulatory dilemma. Exposure response analysis submitted in the New Drug Application was applied to confirm the evidence of efficacy across these dose levels. Modeling and simulation analyses showed that the two studies corroborate each other with respect to the pain relief profiles. The use of PK/PD information confirmed evidence of efficacy across the three studied doses, eliminating the need for additional clinical trials and thus supporting the approval of the product. It can be speculated that the work by Lewis Sheiner reflected in the FDA document titled "Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products" made this scientific approach to the drug approval process possible.
Miller
Raymond
R
Pfizer Global Research and Development, Pfizer Inc, Ann Arbor, MI 48105, USA. raymond.miller@pfizer.com
Ewy
Wayne
W
Corrigan
Brian W
BW
Ouellet
Daniele
D
Hermann
David
D
Kowalski
Kenneth G
KG
Lockwood
Peter
P
Koup
Jeffrey R
JR
Donevan
Sean
S
El-Kattan
Ayman
A
Li
Cheryl S W
CS
Werth
John L
JL
Feltner
Douglas E
DE
Lalonde
Richard L
RL
D000544:Alzheimer Disease / Q000188:drug therapy; D000588:Amines / Q000494:pharmacology; D000818:Animals; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D003509:Cyclohexanecarboxylic Acids / Q000494:pharmacology; D003658:Decision Making, Computer-Assisted; D018691:Excitatory Amino Acid Antagonists / Q000494:pharmacology; D000077206:Gabapentin; D006566:Herpesviridae Infections / Q000150:complications; D006801:Humans; D015233:Models, Statistical; D009437:Neuralgia / Q000188:drug therapy / Q000209:etiology; D010600:Pharmacology / Q000706:statistics & numerical data*; D012984:Software; D005680:gamma-Aminobutyric Acid / Q000494:pharmacology
D000588:Amines; D003509:Cyclohexanecarboxylic Acids; D018691:Excitatory Amino Acid Antagonists; D005680:gamma-Aminobutyric Acid; D000077206:Gabapentin