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Optimizing Drug Development Decisions: Looking Back at 2016

Was it just me or did 2016 just seem to fly by? Reflecting on the events of the past year, I stumbled across this quote which seemed appropriate:

There are years that ask questions and years that answer.

[Zora Neale Hurston, Their Eyes Were Watching God]

Our mission at Certara is helping our clients optimize their drug development decisions to help them bring safer, more effective medications to patients. So, I asked some of my colleagues: “With respect to our mission, was 2016 a year that asked questions or a year that provided answers?” Here are some of their responses:

  • “I’d say that 2016 provided some answers to the question ‘When does a drug program achieve success?’ 2016 was a pivotal year for evidence-based medicine. Regulatory approval is but the first step toward return on pharmaceutical R&D investments. Often, the higher hurdle is convincing payers that the benefits of new therapies are sufficiently favorable relative to existing therapies to justify their higher prices. And proprietary data alone is generally insufficient to make your case. Increasingly, model-based meta-analysis is employed to synthesize and contextualize the body of therapeutic knowledge. The recent merger of Quintiles and IMS Health further highlights the industry’s growing awareness that evidence-based decision-making is the foundation of coherent health care policy.” — Mark Lovern, Executive Director at Certara Strategic Consulting
  • “Answers. I can’t believe that it’s already been a year since we joined the Certara family through the acquisition of XenologiQ, a UK-based quantitative systems pharmacology (QSP) consultancy. QSP technology integrates quantitative knowledge about a compound with the understanding of its mechanism of action for a specific disease and patient population. It focuses on the behavior of biological systems as a whole versus the behavior of individual components and provides the quantitative basis for precision medicine. In 2016, the new Certara QSP team more than doubled in size. Our team collaborated with 20 companies to help answer their specific questions in drug discovery and development, for example, in finding optimal combination therapies in immuno-oncology. I am excited about further developing our QSP capabilities in 2017!” — Piet van der Graaf, Vice President of Quantitative Systems Pharmacology
  • “As a scientist, I strongly believe that answers cannot be general or definitive. With that in mind, will a given year ever provide answers? I sincerely doubt it. Hopefully, this permanent doubt is a key-driver of scientific questioning, and questioning itself will nourish Research.” — Henri Merdjan, Executive Director at Certara Strategic Consulting
  • “Without a good question, answers may not be what you want them to be, and subsequently, the quality of decision-making based on the provided answers will suffer. I think 2016 was an exciting year for Certara. Our growth as an organization allows us to help our clients make decisions along the entire drug life cycle and across different areas of expertise and development. With this breadth of knowledge comes even more responsibility to not only to answer the questions our clients are faced with, but also to make sure they are the right questions.”— Leon Bax, Director at Certara Strategic Consulting
  • “What questions were answered in 2016 and what questions were brought in? In science, there are always questions that are answered to some extent. But, what constitutes a full answer for Person A, might be a partial or incomplete answer for Person B. Some of the questions that were answered, at least in part, in 2016 include the following:
    • Why do we need to pursue precision public health, not just precision medicine? In a recent article in the American Journal of Preventive Medicine, Muin Khoury, Michael Iademarco, and William Riley argue for using the same technologies that are being leveraged to advance precision medicine to advance precision health― providing the right intervention to the right population at the right time. Too many people are still dying from modifiable, known risk factors such as smoking, poor diet, and inadequate physical activity. Better methods for measuring disease, pathogens, and health behaviors could support better assessment of public health problems and developing targeted interventions that promote health and prevent disease.
    • Is crowd-sourcing and sharing clinical trial data a viable model? An article published last month in the Lancet by Guinney and colleagues suggests that the answer may be “Yes!” The open-data, crowd-sourced DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge partnered with Project Data Sphere, an initiative to share data from cancer clinical trials with scientists, to identify a better prognostic model for predicting survival in patients with metastatic castration-resistant prostate cancer. 50 independent methods were developed to predict overall survival and were evaluated in the DREAM challenge. The best performing model uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Meta-analysis of all methods also revealed a previously under-reported, prognostic biomarker. These results suggest that data-sharing can be combined with crowd-sourcing to support developing new prognostic disease models.
    • Is machine learning “magic”? Can it do any good? Again, the results of a paper published by Chekroud and coworkers earlier this year in the Lancet Psychiatry imply that while machine learning may not be a magic bullet, it may be a useful approach for prospectively identifying patients who are likely to respond to a treatment. At present, clinicians have no way of knowing whether a patient will respond to a specific antidepressant. Chekroud and colleagues used patient-reported data from patients with depression to identify variables that predicted treatment outcomes. They then used these variables to train a machine-learning model to predict clinical remission. The model predicted outcomes in clinical trial cohorts for two different antidepressants with accuracy significantly above chance. Using machine-learning to mine existing clinical trial data shows potential for helping clinicians identify which patients are more likely to respond to a medication. This will save money and reduce unnecessary toxicity in patients who were unlikely to respond to a treatment.

    I see 2016 as the year that data science (machine learning, crowd-sourcing) started to merge with traditional clinical trial/medicine analyses.” —Samer Mouksassi, Director at Certara Strategic Consulting

  • “In the novel, Their Eyes Were Watching God, the protagonist, Janie Crawford, returns to her hometown after a long absence. The townspeople make Janie the focus of gossip and speculation about her whereabouts and the absence of her husband. During this period of intense scrutiny, Janie states, ‘There are years that ask questions and years that answer.’ That simple statement applies not only to the fictional situation of town gossip, but also to scientific disciplines including pharmacometrics. Was 2016 a year that “asks questions” or one that “provides answers”? I suggest that it is the latter.
    • In 2016, we saw explosive, global growth in the scientific societies and meetings associated with pharmacometrics. The Population Approach Group in Europe (PAGE), the oldest, dedicated pharmacometrics organization, held its annual meeting in Lisboa, Portugal in June, and it was the largest meeting in over 20 years. In August, the World Congress on Pharmacometrics, organized by the Population Approach Group of Australia and New Zealand (PAGANZ), had over 800 attendees. And last, but not least, the American Conference on Pharmacometrics (ACoP7), organized by the International Society of Pharmacometrics (ISOP), was held in October and had nearly as many attendees as the PAGE meeting. While conference attendance is not the only metric of scientific interest, it strongly suggests that pharmacometrics is growing in influence in the pharmaceutical industry as we work to solve difficult biological problems with model-based analysis techniques.
    • Just recently, the FDA released a new guidance related to the format and content of physiologically-based pharmacokinetic analyses (PBPK) in regulatory submissions. The recognition from regulators that ‘Throughout a drug’s life cycle, PBPK model predictions can be used to support decisions on whether, when, and how to conduct certain clinical pharmacology studies, and to support dosing recommendations in product labeling‘ is an enormous step forward. We are now integrating model-based analysis into treatment decisions for patients. This marks an important step as we utilize pharmacometrics not only to run more efficient and effective clinical trials, but also to enhance a patient’s quality of life through optimization of treatment and minimization of side effects.

Finally, for Certara, we released a major update to our leading pharmacometrics analysis tool, Phoenix, that adds significant power, flexibility, and value to pharmacometricians around the world. Improving the tools that allow pharmacometricians to build models is the primary focus of our software development team. These advances along with the emergence of cloud-based computing have allowed scientists to tackle ever larger real-world problems that were unimaginable only a few years ago.

Yes, 2016 was a year that answers … but in science, answering questions only brings new hypotheses to the table. And that generates more questions … so, 2017, what questions do you need us to answer now?” — Nathan Teuscher, Vice President of Pharmacometric Solutions

As you can see, we’re brimming with ideas about the coming changes in 2017 that will surely transform the approach to drug development. From all of us at Certara, we wish you and your families a Happy New Year!

To learn more about how to systematically leverage the many benefits of M&S across a drug development program, read this white paper.

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By: Suzanne Minton

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