Leveraging Model-based Meta-analysis to Inform Drug Development Decisions

Leveraging Model-based Meta-analysis to Inform Drug Development Decisions

Model-based Meta-analysis (MBMA) is a quantitative framework that uses PK/PD and statistical modeling for leveraging external clinical trial efficacy, tolerability, and safety data to inform drug development decisions. MBMA augments proprietary in-house clinical trial data by systematically searching and tabulating summary results from public sources. These data are then analyzed to characterize the impacts of drug class, drug, dose, and time on the response(s) of interest, plus the potential influence of study population characteristics or the trial conduct. Most important, MBMA provides a quantitative understanding of how a new compound may perform relative to the standard of care and other developmental compounds.

MBMA Can Help Inform Drug Development Decisions

How can MBMA inform strategic drug development decisions?

The foundation of MBMA lies in leveraging external, summary-level data from independent studies data to inform drug development decisions relating to several key questions. How does our novel compound compare to the standard of care treatments? How do drug classes differ with respect to their safety and efficacy profiles within specific indications? How do various efficacy endpoints relate to one another? How do trial design and patient characteristics impact clinical outcomes? May we identify sources of variability? May we characterize placebo and treatment effects?

A database based on public clinical and preclinical data, literature, or published trial information may be used to develop a model that can simulate efficacy and other outcomes parameters. When leveraged with drug development learnings, MBMA, through an iterative approach, can help to inform compound portfolio decision making, go/no go decisions, trial/design characteristics, and provide a better understanding of the competitive landscape.

The advantage of using MBMA versus traditional meta-analysis approaches

Traditional approaches for assessing novel compounds rely on pairwise or network meta-analysis. Pairwise meta-analysis examines interventions or trial arms in pairs. Although this approach is quick and straightforward, it only considers paired intervention-versus-control evidence. Thus, it does not allow indirect comparisons of drugs that have not been compared in a clinical trial. Network meta-analysis combines studies in a network and builds a statistical framework to support indirect comparisons between drugs that may not have been evaluated head-to-head in clinical trials.

The advantage and added value of MBMA ­– an extension of network meta-analysis – is its incorporation of parametric models for the effect of treatment, time, and patient population characteristics. Thus, MBMA not only compares treatments that have not been studied together in a clinical trial. MBMA may also add pharmacological data such as dose-response relationships and time dependencies, model multiple endpoints, and linking biomarkers to clinical endpoints.1

How can MBMA accelerate clinical development?

Sponsors use MBMA to inform developing novel drugs for a range of therapeutic areas including musculoskeletal, auto-immune2, cardiovascular, metabolic diseases, CNS, and pain3. Here are a few examples of how MBMA has impacted drug development for indications in these areas.

  1. Osteoporosis: MBMA was used to run virtual head-to-head trials for comparing denosumab, an approved osteoporosis drug, to drugs in the same competitive landscape. The osteoporosis drug market is crowded with many approved drugs with varying mechanisms of action. Since denosumab had not been compared in clinical trials to other approved osteoporosis treatments, the primary goal of the MBMA study was to compare the time course of biomarkers for measuring the efficacy of osteoporosis drugs – lumbar spine (LS) and total hip (TH) bone mineral density (BMD) changes – during treatment with denosumab or other osteoporosis drugs. Comparing changes in BMD provided insight into the effect of dose, dose frequency, and route of administration. The MBMA used data from 142 clinical trials for preventing or treating postmenopausal osteoporosis. The dose-response relationship for denosumab showed that the approved dosing regimen resulted in maximal BMD changes. The MBMA showed that three years of treatment with denosumab resulted in bigger changes in LS and TH BMD compared to the same treatment duration with competing osteoporosis drugs approved in the US. The MBMA analysis also provided insight into how denosumab compares to other approved osteoporosis drugs without having to spend the time and money on running head-to-head trials.
  2. Psoriasis: An MBMA study was used to support dose optimization and product positioning of a psoriasis drug. The dose-range for Phase 2 studies of the novel psoriasis drug was selected using Phase 1b data. The Phase 1b data demonstrated a strong proof-of-concept for drug efficacy and all active treatments resulted in a maximal therapeutic effect by the end of the study. The MBMA comparator analysis enabled proceeding to Phase 2 trials with a dosing range that would include the best likely dose to carry into Phase 3 trials. MBMA has also been used to evaluate other auto-immune disease treatments such as rheumatoid arthritis, ankylosing spondylitis, psoriasis, and psoriasis arthritis.4,5
  3. Diabetes: MBMA has also been used to quantify the time course of dose vs body weight for anti-diabetic agents, and to support systems pharmacology model development and glucose clamp trial designs for novel insulins.

 

Conclusion

MBMA provides valuable information to better understand your compound and the competitive landscape using public preclinical and clinical data with in-house proprietary data. The resulting information makes best use of all available safety, efficacy and market data to inform strategic drug development and positioning.

 

References

  1. Lovern M. (2019). How Model-Based Meta-Analysis Leverages Public Data to Support Strategic Drug Development Decision Making. AAPS Newsmagazine, May 2019
  2. Visser SAG, deAlwis DP, Stone JA, and Allerheilegen, SA. (2014). Implementation of Quantitative and Systems Pharmacology in Large Pharma. CPT Pharmacometrics and Syst. Pharmacol., 3(10), e142.
  3. Mercier F, Claret L, Prins, K, and Bruno R. (2014). A Model-Based Meta-analysis to Compare Efficacy and Tolerability of Tramadol and Tapentadol for the Treatment of Chronic Non-Malignant Pain. Pain and Therapy, 3(1), 31-44.
  4. Demin I., Hamren B., Luttringer O., Pillai G, and Jung T. (2012). Longitudinal Model-based Meta-analysis of rheumatoid arthritis: an application toward Model-based Drug Development. Pharmacol. Ther., 92, 352-359.
  5. Wang Y, Zhu, R, Xiao J, Davis JC, Mandema J, Jin J, and Tang MT (2016). Short‐Term Efficacy Reliably Predicts Long‐Term Clinical Benefit in Rheumatoid Arthritis Clinical Trials as Demonstrated by Model‐Based Meta‐ J. Clin. Pharmacol., 56(7), 835-844.

To learn more about how MBMA can increase the likelihood of commercial success in drug development, watch this webinar.

Richard C Franzese

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Richard C Franzese

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Dr. Richard C Franzese is a consultant at Certara based in Nashville, TN. He has expertise in population PK, PK/PD modeling and simulation, and model-based meta-analysis. Richard holds a master’s degree in physics and a doctorate in engineering, both from the University of Oxford. He has therapeutic expertise in infectious disease and cardiovascular disease.