Quantitative Systems Pharmacology: Progress Towards Integration into Drug Discovery and Development

Quantitative Systems Pharmacology: Progress Towards Integration into Drug Discovery and Development

Tuesday, May 26, 2015

The New York Academy of Sciences

Presented By

Presented by the Biochemical Pharmacology Discussion Group and the New York Academy of Sciences

 

Quantitative Systems Pharmacology (QSP) is a mathematical modeling approach to translational medicine that integrates quantitative knowledge about a compound with an understanding of its mechanism of action in the context of human disease. The goal of QSP modeling is "to understand, in a precise, predictive manner, how drugs modulate cellular networks in space and time and how they impact human pathophysiology". In the five years since the NIH QSP Working Group last met on this topic, there has been steady progress in both academic research and in the application to drug discovery and development. Despite these successes, full industry-wide adoption of QSP methods has yet to be realized. This symposium highlights advances in QSP applications in the continuum from preclinical exploration to clinical research and includes academic, government, and industry perspectives on the benefits and challenges of full adoption. Case studies from several therapeutic areas will be presented with applications to drug safety, efficacy, and precision medicine.

This event will also be broadcast as a webinar.

Please note: Transmission of presentations via the webinar is subject to individual consent by the speakers. Therefore, we cannot guarantee that every speaker's presentation will be broadcast in full via the webinar. To access all speakers' presentations in full, we invite you to attend the live event in New York City when possible.

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The Biochemical Pharmacology Discussion Group is proudly supported by



  • Pfizer
  • Pfizer
  • Pfizer

Agenda

* Presentation titles and times are subject to change.


May 26, 2015

8:00 AM

Registration and Continental Breakfast

8:45 AM

Introductory Remarks
Sonya Dougal, PhD, New York Academy of Sciences
Joshua Apgar, PhD, Applied BioMath

9:00 AM

Pharmacology at the Single Cell Level
Peter Sorger, PhD, Harvard Medical School

9:40 AM

QSP Modeling to Manage Hepatotoxicity in Drug Development
Paul Watkins, MD, The Hamner Institute

10:20 AM

Networking Coffee Break

 

Four Vignettes on Late Breaking Topics

10:50 AM

Informing Clinical Development of a Novel Agent for Chronic Kidney Disease with Systems Pharmacology Modeling
Richard Allen, PhD, Pfizer

11:10 AM

A Regulatory Application of a Quantitative Systems Pharmacology Model in Assessing the Dosing Regimen for a Recombinant Human Parathyroid Hormone
Nitin Mehrotra, PhD, US Food and Drug Administration

11:30 AM

PredicTox: A Systems Pharmacology Project to Examine Cardiotoxicity Associated with Tyrosine Kinase Inhibitors
Sian Ratcliffe, PhD, Pfizer

11:50 AM

Model-Informed Drug Discovery and Development at Merck - Enhancing the Predictive Value of Discovery Research through Quantitative and Systems Modeling
Matthew L. Rizk, PhD, Merck

12:10 PM

Networking Lunch and Poster Session

1:30 PM

Enhancing Effectiveness of Drug Discovery and Development by Applying Quantitative Systems Modelling
Tim Rolph, PhD, Pfizer

2:10 PM

Quantitative Systems Approaches in Inflammation to Enable Decision Making in Early Discovery and Clinical Trials
John Burke, PhD, Applied BioMath

2:50 PM

Networking Coffee Break

3:20 PM

FDA Perspective on Quantitative Systems Pharmacology Models in Regulatory Submissions
Vikram Sinha, PhD, US Food and Drug Administration

4:00 PM

Introduction to the Panel Discussion
Cynthia Musante, PhD, Pfizer

4:10 PM

Panel Discussion: The Present and Future of Quantitative Systems Pharmacology in Drug Discovery and Development

5:00 PM

Networking Reception

6:00 PM

Adjourn

Speakers

Organizers

Joshua Apgar, PhD

Applied BioMath

Mercedes Beyna, MS

Pfizer, Inc.

John Burke, PhD

Applied BioMath

John M. Burke, PhD, is President, CEO and Co-founder of Applied BioMath, a Systems Biology and Pharmacology company. Dr. Burke’s BS and MS are in Applied Mathematics, University of Massachusetts, Lowell. His PhD degree is in Applied Mathematics, Arizona State University, where he studied dynamical systems, singular perturbation theory, and control of signal transduction networks and protein expression. Prior to Applied BioMath, Dr. Burke joined Boehringer Ingelheim (BI), as Global Head of Systems Biology, where he started, developed and managed the Systems Biology and Pharmacology group, portfolio, strategy and tactics. At BI, his group supported over 100 projects and over 11 transitions into Development or Clinical Trials in five years. Prior to BI, he was a Sr. Fellow in Douglas A. Lauffenburger’s lab, Biological Engineering Department, MIT; Co-Scientific Director of the Cell Decision Processes Center, Systems Biology Department, Harvard Medical School; then Merrimack Pharmaceuticals. He provided systems consulting support for drug discovery companies, including AstraZeneca, Pfizer, and Momenta, while at MIT and HMS. He presently serves on advisory boards for the MIT “Human Physiome on a Chip” MIT-DARPA Program and the Mathematics Department at the University of Massachusetts, Lowell, and is Adjunct Faculty at UMass, Lowell.

Nahor Haddish-Berhane, PhD

Johnson & Johnson

Cynthia Musante, PhD

Pfizer, Inc.

Sonya Dougal, PhD

The New York Academy of Sciences

Speakers

Richard Allen, PhD

Pfizer, Inc.

Richard is a Principal Scientist within Pfizer’s Cardiovascular and Metabolic Research Unit. At Pfizer, he applies quantitative approaches, such as mathematical modeling, to address crucial questions in drug discovery and development. Prior to joining Pfizer Richard was a postdoctoral researcher at Department of Pharmacology, UNC, where he used modeling and image analysis to probe the mechanisms that control cell migration. Richard got his Ph.D. in mathematical biology from University College London.

John Burke, PhD

Applied BioMath

John M. Burke, PhD, is President, CEO and Co-founder of Applied BioMath, a Systems Biology and Pharmacology company. Dr. Burke’s BS and MS are in Applied Mathematics, University of Massachusetts, Lowell. His PhD degree is in Applied Mathematics, Arizona State University, where he studied dynamical systems, singular perturbation theory, and control of signal transduction networks and protein expression. Prior to Applied BioMath, Dr. Burke joined Boehringer Ingelheim (BI), as Global Head of Systems Biology, where he started, developed and managed the Systems Biology and Pharmacology group, portfolio, strategy and tactics. At BI, his group supported over 100 projects and over 11 transitions into Development or Clinical Trials in five years. Prior to BI, he was a Sr. Fellow in Douglas A. Lauffenburger’s lab, Biological Engineering Department, MIT; Co-Scientific Director of the Cell Decision Processes Center, Systems Biology Department, Harvard Medical School; then Merrimack Pharmaceuticals. He provided systems consulting support for drug discovery companies, including AstraZeneca, Pfizer, and Momenta, while at MIT and HMS. He presently serves on advisory boards for the MIT “Human Physiome on a Chip” MIT-DARPA Program and the Mathematics Department at the University of Massachusetts, Lowell, and is Adjunct Faculty at UMass, Lowell.

Nitin Mehrotra, PhD

US Food and Drug Administration (FDA)

Dr. Nitin Mehrotra is a Team Leader in Division of Pharmacometrics, Office of Clinical Pharmacology at the US FDA. He joined FDA in 2007. Prior to joining FDA, Dr. Mehrotra obtained his Ph.D. degree from Birla Institute of Technology and Sciences, India. Following which, he worked as a post-doctoral fellow at the UTHSC where he focused on the utilization of Pharmacometrics in (1) developing dosing guidelines for pediatrics and (2) development of a multifactorial index to predict mortality subjects with obstructive airway disease. In his current role at the FDA, Dr. Mehrotra works in the areas of Oncology, Metabolic and Endocrinology, gastroenterology and inborn error products. His job profile includes applying pharmacometrics for regulatory decisions such as dose selection, evidence of effectiveness, trial design, etc. Dr. Mehrotra is a strong proponent of the concept of ‘rationale dose selection’ and believes it to be pivotal for success of any drug development program. He has published several articles in the area of pharmacometrics and has been invited to national and international conferences to present on topics pertaining to application of pharmacometrics in drug development and regulatory decisions.

Sian Ratcliffe, BSc, PhD

Pfizer Inc.

Dr. Sian Ratcliffe is Global Head of Safety Pharmacology CoE in Drug Safety R&D at Pfizer, Groton, Connecticut, US

Dr. Ratcliffe's Safety Pharmacology group spans preclinical safety disciplines of In Vitro profiling, Electrophysiology, Cardiovascular, Neuropharmacology and Physiology.

In her 15 year tenure at Pfizer, Sian has also held other senior leadership roles in Clinical, Safety Risk Management and Regulatory Affairs, managing projects throughout development stages from research to post-approval in the Neurology, Psychiatry, Pain, Women’s Health and Respiratory therapeutic areas. Sian has a keen interest in translational and predictive safety analyses, systems pharmacology and data mining approaches. Sian is an active member of the internal Neuropsychiatric and Abuse Potential Advisory Council, with a particular focus on detection of treatment emergent suicidal ideation and behavior as well as Human Abuse Liability study methodology and the use of novel data mining and statistical techniques to assess abuse potential. Prior to joining Pfizer, Sian worked for Elsevier as an editor for a number of the Trends journals. Sian has a PhD in Pharmacology from the University of Cambridge (1996) where she also held post-doctoral research and academic posts.

Tim Rolph, PhD

Pfizer, Inc.

Tim Rolph is Vice President, Program Value Enhancement, at Pfizer. Formerly Chief Scientific Officer of Pfizer’s Cardiovascular & Metabolic Disease Research Unit, he established it in Cambridge, Massachusetts. During his leadership, ertuglifozin was discovered and progressed to Ph3 in partnership with Merck as a fixed-dose combination with JanuviaTM.

He received a BSc in Biochemistry from University of London (UK), and D.Phil from University of Oxford (UK). His pre-and post-doctoral training was at the Nuffield Institute for Medical Research, studying metabolic adaptations of skeletal & cardiac muscle during development. Subsequently, he joined Glaxo’s veterinary R&D, initially studying modulation of growth for food production, then research for anti-parasitic vaccines against protozoan (anti-coccidial for poultry, ParacoxTM) and metazoan species (gastrointestinal helminths).

Beginning in a similar role at Pfizer, he became leader of human anti-infective research at Sandwich (UK), during which the prototypical CCR5 antagonist, Maraviroc (CelzentryTM), was discovered and launched as a novel antiretroviral for HIV. He then became Head of Research at Pfizer’s Sandwich laboratory and more recently at Groton, CT.  Through his career, he has led groups who have taken many different therapeutic mechanisms into Ph2, covering HIV, diabetes, inflammatory and renal diseases.

Matthew Rizk, PhD

Merck & Co., Inc.

Matt Rizk is a scientist in the Quantitative Pharmacology and Pharmacometrics group within the department of PK, PD and Drug Metabolism at Merck, where he serves as the therapeutic area lead for activities in HCV and antibacterial drug development. Matt obtained his PhD from UCLA in Chemical Engineering, with research focused in the area of synthetic biology. Since obtaining his doctorate, he has been at Merck and served on interdisciplinary drug development teams whose work impacted all areas of drug discovery and development ranging from target identification through all phases of development and post-licensure activities, with contributions primarily in the areas of clinical pharmacology and the modeling and simulation strategy and execution. Matt has had numerous publications and given many presentations, particularly in the areas of antibacterials and HIV.

Vikram Sinha, PhD

US Food and Drug Administration (FDA)

Vikram Sinha, PhD, is the Director, Division of Pharmacometric at the USFDA. In his current role, Vikram leads the Pharmacometrics Division. The Division plays a critical role in understanding the impact of variability in response to drugs and relates it to assessing benefit and risk. He leads a multidisciplinary team of quantitative clinical pharmacologists, statisticians, engineers, and data management experts. Within CDER, pharmacometric work is conducted with the intent to aid the decision to approve and label the drug product. There is particular attention on providing a consulting function on drug dosing for patients and advice on trial design decisions by sponsors. Previously, Vikram was at Eli Lilly, where he was scientific lead for global pharmacokinetics/pharmacodynamics and pharmacometrics. At Lilly, he was accountable for developing quantitative translational strategies, clinical plans, and regulatory strategies in the area of clinical pharmacology. He has 16 years of experience in the pharmaceutical industry. He has made notable contributions to the general scientific community through teaching, publications, and engagement with industry/government consortia dedicated to advancing innovation in the area of drug discovery and development. Vikram earned a bachelor’s degree in pharmacy and a doctorate degree in pharmaceutical sciences from the University of Arizona. He completed post-doctoral training at the University of Nebraska Medical Center.

Peter Sorger, PhD

Harvard Medical School

Peter Sorger, PhD, is a Professor of Systems Biology at Harvard Medical School whose research focuses the systems biology of signal transduction with an emphasis on apoptotic and oncogenic networks in normal and tumor cells. Peter serves as Head of the Harvard Program in Therapeutic Sciences (HiTS) and Director of its Laboratory of Systems Pharmacology. In these roles he leads a university-wide effort to advance basic and translational science used to develop new medicines, evaluate drugs through clinical trials and identify patients most likely to benefit from specific therapies. Peter’s laboratory applies diverse computational and experimental approaches to cancer and inflammatory diseases in human cells and genetically engineered mice. His group also develops open-source software for assembling computable knowledge about signal transduction and for using this knowledge to understand and predict the responses of cells and tissues to therapeutic drugs applied individually and in combination. Sorger was cofounder of Merrimack Pharmaceuticals and Glencoe Software and is an advisor to multiple public and private companies and to research institutes in Europe and Japan.

Paul Watkins, MD

Hamner-UNC Institute for Drug Safety Sciences

Dr. Paul B. Watkins is director of the Hamner-University of North Carolina Institute for Drug Safety Sciences. He is also Professor of Medicine, Pharmacy and Public Health at the University of North Carolina, Chapel Hill. Dr. Watkins is a trained clinical hepatologist and also an accomplished basic and translational investigator in the fields of drug metabolism and hepatotoxicity. He serves as the chair of both the Steering and Genetics Committees for the U.S. Drug-Induced Liver Injury Network (DILIN) (U01DK065201). He is one of the most frequently cited authors in the field of pharmacology according to www.ISIhighlycited.com. He is the recipient of numerous honors and awards including the Therapeutic Frontiers Award from the American College of Pharmacy election to the Association of American Physicians (AAP), the 2013 Agilent Therapeutic Frontiers Award, and he will receive the Rawls-Palmer Award for Progress in Medicine at the 2015 annual meeting of the American Society for Clinical Pharmacology and Therapeutics.

Sponsors

For sponsorship opportunities please contact Perri Wisotsky at pwisotsky@nyas.org or 212.298.8642.

Grant Support

This program is supported in part by a grant from AbbVie and Merck and Co., Inc.

Promotional Partners

American Association of Pharmaceutical Scientists (AAPS)

American Society for Clinical Pharmacology and Therapeutics (ASCPT)

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The Biochemical Pharmacology Discussion Group is proudly supported by

 

  • Pfizer
  • Pfizer
  • Pfizer

Abstracts

QSP Modeling to Manage Hepatoxicity in Drug Development
Paul B. Watkins, MD
Hamner-University of North Carolina Institute for Drug Safety Sciences, Research Triangle Park, North Carolina, USA
University of North Carolina, Chapel Hill, North Carolina, USA

The DILI-sim Initiative is a public-private partnership entering its fourth year with the goal of developing a mechanistic, mathematical model of drug-induced liver injury in mice, rats, dogs and humans.  Mechanisms incorporated into the model include reactive metabolite/oxidative stress, mitochondrial toxicity and alterations in bile acid homeostasis.  The initial efforts focused on species differences in dose-dependent hepatoxicity and improved interpretation of serum biomarkers.   However, there has been recent success in modeling idiosyncratic hepatoxicity, including prolonged latency to injury onset.
 

Informing Clinical Development of a novel agent for Chronic Kidney Disease with Systems Pharmacology Modeling
Richard Allen, PhD, Pfizer Inc., Cambridge, Massachusetts

Clinical development of therapies for the treatment of Chronic Kidney Disease (CKD) is particularly challenging, with no new class of treatments being approved in nearly 20 years. While there are many factors contributing to this, one may be that the Phase II surrogate outcome (proteinuria) could be misleading in assessment of probability of success in Phase III. To mitigate this, and inform the clinical drug development of a novel agent (a PDE5 inhibitor) we collaborated with the Institute of Systems Biology (Moscow) to develop a systems pharmacology model of CKD and its treatments. Here we will discuss the model, its development and applications.
 
Coauthor: CJ Musante, PhD, Pfizer Inc., Cambridge, Massachusetts
 

A regulatory application of a quantitative systems pharmacology model in assessing the dosing regimen for a recombinant human parathyroid hormone
Nitin Mehrotra, PhD, Office of Clinical Pharmacology, United States Food and Drug Administration, USA

The use of quantitative system pharmacology models in regulatory decisions is rare and follows a ‘fit for purpose’ approach. An example is presented where a system pharmacology model was used for regulatory decision, more specifically to assess the adequacy of the proposed dosing regimen. On January 23, 2015, FDA approved Natpara® (recombinant human parathyroid hormone or rh-PTH [1-84]) to be administered as a once daily dosing regimen, as an adjunct to calcium and vitamin D to control hypocalcemia in patients with hypoparathyroidism. The results of the phase 3 trial indicated that although once daily dosing regimen was adequate in reducing the calcium and vitamin D dose requirement while maintaining the mean albumin-corrected total serum calcium within the normal range, the once daily dosing regimen was not adequate to control hypercalciuria. A published systems pharmacology model of calcium homeostasis was used because of the feedback systems modulated by PTH in controlling serum calcium levels; this model was further modified to assess the impact of changing the dosing regimen of rh-PTH [1-84] on hypercalciuria. Based on the simulation results, it was demonstrated that a more frequent dosing regimen may be needed to provide control on hypercalciuria while maintaining normocalcemia. A post-marketing trial was recommended to assess the pharmacokinetics (PK) and pharmacodynamic effects (PD) of Natpara dosing regimen on the control of serum calcium and normalization of calcium excretion in urine.
 
Coauthors: Immo Zadezensky, PhD1, Naomi Lowy MD2, Dragos Roman MD2, Jean-Marc.Guettier MD2, Chandrahas Sahajwalla PhD1, Vikram Sinha PhD1, Manoj Khurana PhD1
1 Office of Clinical Pharmacology, United States Food and Drug Administration, USA
2 Division of Metabolic and Endocrinology Products, Office of New Drugs, United States Food and Drug Administration, USA
 

PredicTox: A Systems Pharmacology Project to Examine Cardiotoxicity Associated with Tyrosine Kinase Inhibitors
Sian Ratcliffe, PhD, Drug Safety R&D, Pfizer

The PredicTox project is a systems pharmacology project that has been identified as a research priority area for FDA as part of its Regulatory Science Strategic Plan in Modernizing Toxicology to Enhance Product Safety. This systems pharmacology project blends biology with computational modelling to untangle intricate interactions between genes, proteins, metabolites and other molecules within cells and as such offers a new approach to understand how drugs can lead to adverse effects.  For an initial proof of concept, PredicTox is focusing on the drug – event pairing of tyrosine kinase inhibitors (TKIs: small molecules and monoclonal antibodies) and cardiac (left ventricular) dysfunction. PredicTox has now moved from concept to reality with the first TranSMART v1.2 use case instance of this project now created, building from publicly available GEO and published clinical study datasets, and the development of a clinical phenotype-based ontology to underpin data curation and analysis. The team is working closely with industry data partners on the next stage of assembling, curating and sharing molecular, preclinical study and clinical trial data from marketed or development TKIs, including raw data from the sunitinib studies. This presentation will introduce and discuss the framework for the PredicTox project, including the future development of systems pharmacology-based predictive models.
 
Coauthors: Darrell Abernethy, MD, PhD2, Keith Burkhart, MD, FACMT2, Nancy Beck, PhD3, Lori Minasian, MD4, Sirarat Sarntivijai, PhD5, Shadia Zaman, PhD2
1 Drug Safety R&D, Pfizer, Groton, Connecticut, United States
2 Office of Clinical Pharmacology, CDER, FDA, Silver Spring, Maryland, United States
3 Reagan-Udall Foundation for the FDA, Washington DC, United States
4 National Cancer Institute, Bethesda, Maryland, United States
5 European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, United Kingdom
 

Model-Informed Drug Discovery and Development at Merck – Enhancing the Predictive Value of Discovery Research through Quantitative and Systems Modeling
Matthew L. Rizk, PhD, Merck & Co., Inc

Quantitative and systems pharmacology concepts and tools are the foundation of the model-informed drug discovery and development paradigm at Merck for integrating knowledge, enabling decisions, and enhancing submissions. Efforts to date have primarily focused on drug models that are instrumental in informing the right molecule and right dose.  Linking these drug models to mathematical representations of the pertinent pathophysiology and to those linking molecular structure to pharmacokinetic properties allow for further exploration of potential pathways or targets and optimal molecular design, two key challenges in drug discovery.  Here we present an overview of the quantitative and systems pharmacology efforts at Merck, with specific case studies and examples of impactful QSP, in silico and translational PK/PD drug exposure-response efforts in both the antibacterial and diabetes therapeutic areas.  These examples represent key steps in realizing the vision of end-to-end model-informed drug discovery and development, where model development commences early and integrates knowledge through the life-cycle of the program.
 
Coauthors: Prajakti Kothare, PhD, Daniel Tatosian, PhD, Brian Topp, PhD, and Sandra R. Allerheiligen, PhD
Merck & Co., Inc., Kenilworth, New Jersey, United States
 

Enhancing Effectiveness of Drug Discovery and Development by Applying Quantitative Systems Modelling
Tim P Rolph, DPhil, Pfizer Worldwide R&D

Quantitative systems modelling can inform and enable all stages of drug discovery and early clinical development. Examples will be presented from therapies for metabolic diseases, ranging from simulating biological activity of an isolated enzyme, to integrated whole body responses to modulation of a specific target or pathway. These illustrate how modelling and simulation has been used to; evaluate ideas for therapeutic targets highlighting knowledge gaps and informing the design of experiments to address these gaps, inform design of drug candidates from both pharmacological and pharmacokinetic standpoints, expedite ‘end-to-end’ project progress, and inform project decisions.
 
Coauthors: Cynthia J Musante, PhD, Tristan S. Maurer, PhD, William S. Denney, PhD
Pfizer Worldwide R&D, 610 Main Street, Cambridge, Massachusetts, USA, 02139
 

Quantitative Systems Approaches in Inflammation to Enable Decision Making in Early Discovery and Clinical Trials
John M Burke, PhD, Applied BioMath, LLC, Winchester, MA, United States

Quantitative systems pharmacology is a holistic mathematical modeling, analysis, and simulation approach that integrates biochemical disease pathomechanisms and drug – target pharmacology with the goal of linking phenotype, and functional outcome. Deep biological insights into a disease can be gained by systematically studying the model’s non-linearity and parameters that describe a drug-disease system, often resulting in hypothesis generation. For example, insights can be gained by mathematically exploring multiple complex in vitro systems that cannot be studied in one assay, by simultaneously ranging drug binding affinities and cell numbers and comparing functional readouts in silico . Here we will show case studies where systems approaches have provided counter intuitive insights that were recapitulated in the lab and facilitated team decisions.
 
Coauthor: Joshua Apgar, PhD, Applied BioMath, LLC, Winchester, MA, United States
 

A Regulatory Perspective on the Use of Systems Pharmacology Approaches
Vikram Sinha PhD, Division of Pharmacometrics, Office of Clinical Pharmacology, CDER, USFDA

Drug development and regulatory decisions are driven by information that is compiled primarily from clinical trials and other supportive experiments, but also through clinical experience in the post-market period. The wisdom of these decisions determines the efficiency of drug development, the decision to approve the drug, and the resultant guidance on how to use the product, in the label.
 
In addition to ensuring quality standards, regulators are looking for evidence regarding appropriate dosing, consistency among multiple end points and evidence that benefits exceed harms. Many of these elements can be ascertained before a phase 3 trial is conducted. Indeed, what constitutes confirmatory evidence in support of confirmatory trials has been a subject of much debate. Since the first days of the science of pharmacology, evidence of “dose-response” has always constituted the strongest possible positive evidence of a pharmacologic mechanism of action. As our understanding of pharmacologic and pathophysiologic mechanisms increases and more drugs are designed to interact with specific receptors whose links to pathophysiologic mechanisms are well understood, drugs whose pharmacologic mechanism of benefit remains uncertain will constitute a smaller fraction of candidates for development and approval. Thus, systems pharmacology approaches offer an important learning tool into mechanistic insights but require clear expression of assumptions and expectations. The single-most important strength of such analyses is its ability to integrate knowledge across the development program, compounds, and biology.
 
While the decisions are usually simple in nature (e.g., trial design and project progression at the company, product and labeling approval at FDA), the data informing the decision are complex and diverse. Systems pharmacology looks to build a mathematical model of the pertinent physiology and includes a pharmacokinetic/pharmacodynamic drug model that tells us how the compound works. It is used to simulate clinical trials in which the following elements can be tested: 1) Develop and test hypotheses for optimizing dosing, 2) simulate alternative dosing strategies, 3) simulate alternative patient populations and, 3) simulate alternative combination treatments.
 

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