Incorporating patient preferences and burden-of-disease in evaluating ALS drug candidate AMX0035: a Bayesian decision analysis perspective

Abstract

Objective

Provide US FDA and amyotrophic lateral sclerosis (ALS) society with a systematic, transparent, and quantitative framework to evaluate the efficacy of the ALS therapeutic candidate AMX0035 in its phase 2 trial, which showed statistically significant effects (p-value 3%) in slowing the rate of ALS progression on a relatively small sample size of 137 patients.

Methods

We apply Bayesian decision analysis (BDA) to determine the optimal type I error rate (p-value) under which the clinical evidence of AMX0035 supports FDA approval. Using rigorous estimates of ALS disease burden, our BDA framework strikes the optimal balance between FDA’s need to limit adverse effects (type I error) and patients’ need for expedited access to a potentially effective therapy (type II error). We apply BDA to evaluate long-term patient survival based on clinical evidence from AMX0035 and Riluzole.

Results

The BDA-optimal type I error for approving AMX0035 is higher than the 3% p-value reported in the phase 2 trial if the probability of the therapy being effective is at least 30%. Assuming a 50% probability of efficacy and a signal-to-noise ratio of treatment effect between 25% and 50% (benchmark: 33%), the optimal type I error rate ranges from 2.6% to 26.3% (benchmark: 15.4%). The BDA-optimal type I error rate is robust to perturbations in most assumptions except for a probability of efficacy below 5%.

Conclusion

BDA provides a useful framework to incorporate subjective perspectives of ALS patients and objective burden-of-disease metrics to evaluate the therapeutic effects of AMX0035 in its phase 2 trial.

Keywords:

• Amyotrophic lateral sclerosis
• Bayesian decision analysis
• clinical trial development
• patient value
• pharmaceutical regulation
• survival analysis

Acknowledgements

The authors thank two anonymous reviewers for their valuable comments and suggestions. Research support from MIT Laboratory for Financial Engineering is gratefully acknowledged. The views and opinions expressed in this article are those of the authors only, and do not necessarily represent the views and opinions of any institution or agency, any of their affiliates or employees, or any of those acknowledged above.

Declaration of interest

QX reports personal investments in publicly traded pharmaceutical companies. JC and ZBC have no conflict of interest to disclose. AWL reports personal investments in private biotech companies, biotech venture capital funds, and mutual funds. AWL is a co-founder and principal of QLS Advisors LLC, a healthcare investments advisor, and QLS Technologies LLC, a healthcare analytics and consulting company; an advisor to Apricity Health, Aracari Bio, BrightEdge Impact Fund, Enable Medicine, FINRA, Health at Scale, Lazard, NIH/NCATS, Quantile Health, Roivant Social Ventures, SalioGen Therapeutics, Swiss Finance Institute, and Thalēs; and a director of AbCellera, Annual Reviews, Atomwise, BridgeBio Pharma, and Roivant Sciences. During the most recent six-year period, AWL has received speaking/consulting fees, honoraria, or other forms of compensation from: AbCellera, AlphaSimplex Group, Annual Reviews, Apricity Health, Aracari Bio, Atomwise, Bernstein/Fabozzi Jacobs Levy Award, BridgeBio Pharma, Cambridge Associates, Chicago Mercantile Exchange, Enable Medicine, Financial Times, Harvard Kennedy School, IMF, Journal of Investment Management, Lazard, National Bank of Belgium, New Frontier Advisors/Markowitz Award, Oppenheimer, Princeton University Press, Q Group, QLS Advisors, Quantile Health, Research Affiliates, Roivant Sciences, SalioGen Therapeutics, Swiss Finance Institute, and WW Norton.

Data availability statement

The data supporting the results reported in the article can be found in the cited references. The software for Bayesian decision analysis is available upon reasonable request to the corresponding author.

Additional information

Funding

QX, JC, and ZBC gratefully acknowledge research support from MIT Laboratory for Financial Engineering. No direct funding was received for this study and no funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of this manuscript. The authors were personally salaried by their institutions during the period of writing (though no specific salary was set aside or given for the writing of this manuscript).