Application of clinical trial simulation to compare proof-of-concept study designs for drugs with a slow onset of effect; an example in Alzheimer's disease.

OBJECTIVE: Clinical trial simulation (CTS) was used to select a robust design to test the hypothesis that a new treatment was effective for Alzheimer's disease (AD). Typically, a parallel group, placebo controlled, 12-week trial in 200-400 AD patients would be used to establish drug effect relative to placebo (i.e., Ho: Drug Effect = 0). We evaluated if a crossover design would allow smaller and shorter duration trials. MATERIALS AND METHODS: A family of plausible drug and disease models describing the time course of the AD assessment scale (ADAS-Cog) was developed based on Phase I data and literature reports of other treatments for AD. The models included pharmacokinetic, pharmacodynamic, disease progression, and placebo components. Eight alternative trial designs were explored via simulation. One hundred replicates of each combination of drug and disease model and trial design were simulated. A 'positive trial' reflecting drug activity was declared considering both a dose trend test (p < 0.05) and pair-wise comparisons to placebo (p < 0.025). RESULTS: A 4 x 4 Latin Square design was predicted to have at least 80% power to detect activity across a range of drug and disease models. The trial design was subsequently implemented and the trial was completed. Based on the results of the actual trial, a conclusive decision about further development was taken. The crossover design provided enhanced power over a parallel group design due to the lower residual variability. CONCLUSION: CTS aided the decision to use a more efficient proof of concept trial design, leading to savings of up to US 4 M dollars in direct costs and a firm decision 8-12 months earlier than a 12-week parallel group trial.

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.

The use of clinical trial simulation to support dose selection: application to development of a new treatment for chronic neuropathic pain.

PURPOSE: Pregabalin is being evaluated for the treatment of neuropathic pain. Two phase 2 studies were simulated to determine how precisely the dose that caused a one-point reduction in the pain score could be estimated. The likelihood of demonstrating at least a one-point change for each available dose strength was also calculated. METHODS: A pharmacokinetic-pharmacodynamic (PK/PD) model relating pain relief to gabapentin plasma concentrations was derived from a phase 3 study. The PK component of the model was modified to reflect pregabalin PK. The PD component was modified by scaling the gabapentin concentration-effect relationship to reflect pregabalin potency, which was based on preclincal data. Uncertainty about the potency difference and the steepness of the concentration-response slope necessitated simulating a distribution of outcomes for a series of PK/PD models. RESULTS: Analysis of the simulated data suggested that after accounting for the uncertainty, there was an 80% chance that the dose defining the clinical feature was within 45% of the true value. The likelihood of estimating a dose that was within an acceptable predefined precision range relative to a known value approximated 60%. The minimum dose that should be studied to have a reasonable chance of estimating the dose that caused a one-point change was 300 mg. CONCLUSIONS: Doses that identify predefined response may be imprecisely estimated, suggesting that replication of a similar outcome may be elusive in a confirmatory study. Quantification of this precision provides a rationale for phase 2 trial design and dose selection for confirmatory studies.