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Building an MBMA Framework to Support Developing New Neuropathic Pain Drugs
Wed, April 24th

Model-Based Meta-Analysis (MBMA) is a growing methodology that leverages literature based and often external data to provide a quantitative framework to enable strategic drug development decisions. MBMA comparator models often include valuable pieces of information such as time, covariates, clinical trial efficacy and safety information.

Neuropathic pain (NP) is a pain initiated or caused by primary lesion(s) in the nervous system and is characterized by a presence of increased activity and dysfunction of peripheral sensory nerves within the CNS. The term neuropathic pain is used to categorize a collection of chronic pain syndromes such as diabetic peripheral neuropathy (DPN), post herpetic neuralgia (PHN) and fibromyalgia. The clinical development of novel neuropathic pain treatment is challenging and complicated by several factors including but not limited to patient reported outcomes, large placebo effects, varying baseline pain scores, varying methods for measuring response to treatments, lack of active comparator controls in development, and intrinsic factors.

Pain is subjective in nature. Therefore, use of an adequate instrument to measure the primary endpoint is critical to evaluate efficacy.  Pain intensity or reduction in pain intensity is the fundamental measure of efficacy in analgesia. In addition, elements of trial design such as selection of appropriate primary and secondary endpoints, order of treatment, treatment duration, and allowed concomitant medications can influence a trial’s efficacy readout.

Many of these aspects make it difficult to compare trials head to head. MBMA provides a framework to compare treatments in silico that have not been studied in the same clinical trial and account for sources of potential variability.

In this webinar, Leticia Arrington (Merck) and Richard Franzese (Certara) discussed data considerations, analysis methods, and learnings from a recent network MBMA analysis of relevant endpoints in NP that will be used for general competitive landscaping questions to support Merck’s ongoing mission to develop better NP therapies.


Leticia Arrington is a Senior Scientist in the Pharmacometrics group of the department of Quantitative Pharmacology and Pharmacometrics at Merck Co. & Inc. She obtained her MSc. degree in Pharmacology from Thomas Jefferson University. Leticia is currently a PhD student studying item response theory and clinical trial optimization under supervision of Dr. Mats Karlsson in the pharmacometrics research group at Uppsala University. She has over ten years of industrial drug development experience. Her interests include PK/PD modeling and disease progression modeling, and she is passionate about applying her pharmacometrics expertise to impact drug development in the neuroscience and infectious disease areas.

Richard C. Franzese, DPhil, joined Certara in June 2017. He has expertise in model-based meta-analysis and population PK/PD modeling and simulation. His work covers areas including infectious diseases, vascular diseases, and neuroscience at various phases of drug development including regulatory submission. Richard was trained as a physicist at the University of Oxford. He focused on biological and condensed matter physics before completing his DPhil in Engineering Science as part of the interdisciplinary Life Sciences Interface Doctoral Training Centre, led by Professor David Gavaghan. Richard has conducted research in Computational Biochemistry and Materials Science in addition to his DPhil research which utilized mixed-effects modeling and dynamics systems theory in Biomechanics. Richard enjoys running and cycling in his free time; he was a competitive track and cross-country runner for 10 years and won the Oxford-Cambridge Varsity Cross Country and Mile races. Richard has also taught mathematics and coached track and field.




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