Using PML to Perform Mechanistic Pharmacokinetic Modeling

Speaker(s): Cen Guo
Date: January 31, 2018
Time: 11 am EST
Duration: 1 hour

Transport proteins (transporters) play a vital role in governing drug concentrations in the blood and in various organs including the liver, brain, intestine, lung and kidney. Transporters can move drugs into the tissues (increasing tissue drug levels) and also remove drugs (reducing tissue levels) depending on the location and function of the transporter within the tissue. These proteins often modulate intestinal drug absorption, hepatic/renal elimination, and can enhance the effectiveness of cholesterol-lowering statin drugs.

Assessing the drug interaction potential of investigational drug products is a critical step in their development. The recently published FDA guidance “In Vitro Metabolism- and Transporter-mediated Drug-drug Interaction Studies” addresses how to use in vitro methods to evaluate potential interactions between investigational drugs and transporters.

Drug-mediated inhibition of bile acid transporters in the liver affects bile acid homeostasis, which has important implications for safe and efficacious drug therapy. Current methods to predict these interactions are limited by the interplay of multiple transporters and inaccurate estimates of the relevant inhibitor concentrations. There is no consensus on which type of inhibitor concentration (total or unbound; cellular or cytosolic) is optimal to use for predicting the inhibition of efflux transporters.

Join this webinar with Cen Guo—a graduate student at UNC-Chapel Hill—to learn how she used an integrated approach to predict alterations in bile acid disposition due to inhibition of multiple transporters using the model bile acid taurocholate (TCA). TCA pharmacokinetic (PK) parameters were estimated by mechanistic PK modeling using data from sandwich-cultured human hepatocytes. Monte Carlo simulations of TCA disposition in the presence of model inhibitors (telmisartan and bosentan) were performed using inhibition constants for TCA transporters and inhibitor concentrations including cellular total or unbound concentrations and cytosolic total or unbound concentrations.

Phoenix WinNonlin® uses Phoenix Modeling Language (PML) to encode pharmacokinetic and pharmacodynamic models. Although most models can be built using the graphical user interface (GUI) in Phoenix, some models require custom coding with PML. By attending this webinar, you will learn how to use PML to perform mechanistic pharmacokinetic modeling.

About Our Speaker

Cen Guo is a fifth-year PhD candidate in Pharmaceutical Sciences at the University of North Carolina at Chapel Hill. She is a Chancellor’s Fellow from Royster Society of Fellows. Under the guidance of Dr. Kim Brouwer, Associate Dean for Research and Graduate Education, Guo’s dissertation research focuses on hepatic transporters and pharmacokinetic modeling. Prior to attending UNC, Guo received her BS in pharmacy in 2010 and her MS in pharmacokinetics in 2013 from China Pharmaceutical University.


Apocalyptic Clinical Pharmacology: A Comprehensive Approach to Drug Development

Speaker(s): Graham Scott
Date: February 21, 2018
Time: 11 am EST
Duration: 1 hour

Imagine a virtual biological world with all of the exquisite features of physiology, biochemistry and anatomy of our “wonderfully made” human bodies. Candidate drugs are inserted into this biological system. The interaction between our investigational drug and the biological system is fixed by the chemistry of the drug and the system into which it is placed. If we knew everything about the biological system, drug development would be complete before we started!

The problem is that we don’t.

Our task in drug development is to uncover this drug-system interaction to discover the risk-benefit profile investigational drugs offer to patients. The drug-system interaction is exceedingly complex, but the variables open to the uncovering process are surprisingly few. On the drug side, we can vary the dose, the frequency of administration, the route of administration, and the formulation. On the system side, we can vary the disease population, the demographics of that population, and some features of the patient’s environment.

When the clinical pharmacology experts at regulatory agencies review submissions, they approach the task by asking four questions:

  1. To what extent does the available clinical pharmacology information provide pivotal or supportive evidence of effectiveness?
  2. Is the proposed dosing regimen appropriate for the general patient population for which the indication is being sought?
  3. Is an alternative dosing regimen and/or management strategy required for subpopulations based on intrinsic factors?
  4. Are there clinically relevant food-drug or drug-drug interactions, and what is the appropriate management strategy?

One approach to this uncovering process would be to test every possible dose, at every possible frequency, to every possible patient group including every possible demographic feature (large, small, young, old, black, white, etc.) under every possible environmental condition (eg, co-administration with every other drug possible, every possible meal combination for oral drugs, etc.). Such an approach would uncover all we would need to know, but it would hardly be practical.



So what’s the answer?

To find out, join this webinar with Dr. Graham Scott, Senior Director of Clinical Pharmacology at Certara Strategic Consulting to learn why you should invest in “apocalyptic clinical pharmacology.” “Apocalypse” is derived from the Greek apokálypsis (ἀποκάλυψις)—a combination of ἀπό and καλύπτω, which literally means “uncovering.”

An investigational drug is exactly the same compound at the candidate selection stage as it is when the development program is complete. If this is the case, then it begs the question as to what development actually is. Clinical Pharmacology is fundamentally a discipline that is all about uncovering; it really is apocalyptic!

In this webinar, Dr. Scott will present case studies that illustrate how sponsors can benefit from a clinical pharmacology strategy that uses model-informed approaches to inform and “fill in the gaps” of clinical trials.

About Our Speaker

As a Senior Director of Clinical Pharmacology, Dr. Graham Scott is strategically growing the UK team. Most of his time is spent leading and guiding the team, as well as engaging clients throughout Europe. Dr. Scott has more than 30 years’ experience in the Pharmaceutical industry, having worked in various roles in pre-clinical, early clinical, and clinical pharmacology drug development. He has varied and deep experience in early clinical development, having led more than 30 FIM studies and “early-in-human” studies. He has interacted with all major health regulatory authorities, having led and overseen multiple filings. His work experience has been with the top 20 pharma companies in UK, USA, and mainland Europe. Most recently, Dr. Scott led Takeda’s European clinical pharmacology team and one of their global clinical pharmacology therapeutic areas. He has completed a leadership program at INSEAD, is a member of the Royal Pharmaceutical Society, and obtained a PhD in drug metabolism from the University of Strathclyde.


Estimating the Number of Transit Compartments Using a Distributed Delay Model

Speaker(s): Wojciech Krzyzanski
Date: February 28, 2018
Time: 11 am EDT
Duration: 1 hour

Delays are ubiquitous in pharmacokinetic (PK) and pharmacodynamic (PD) studies. Transit compartment models, described by systems of ordinary differential equations, have been widely used to describe delayed outcomes in PK and PD studies. This type of model has the disadvantage of requiring manually finding proper values for the number of compartments. In addition, transit compartment models may require many differential equations to fit the data and may not adequately describe some complex features.

Delay differential equations have been widely used in the biological sciences and engineering to model delayed outcomes. This approach that does not suffer the disadvantages incurred by using transit compartment models. Differential equations that only involve discrete delays are called discrete delay differential equations. The distributed delay approach includes the discrete delay approach as a special case. This is done through assuming a specific distribution form for the delay time.

The maturation of blood cells from the early stage precursors in the bone marrow to the mature cells observed in the circulation is an example of a system that exhibits delays. For example, red blood cells develop from precursors in the bone marrow. Upon the stimulation with hematopoietic growth factors, they mature and are released to the circulation where they carry hemoglobin. Likewise, white blood cells originate from myeloid stem cells. Upon stimulation by cytokines, they mature and are released from the bone marrow to the circulation. In drug development, a common application of models incorporating delays is to evaluate the effects of drugs on hematopoietic cells. This application has particular use in oncology drug development as many chemotherapeutic agents are toxic to hematopoietic cells.

The gamma-distributed delay model has been introduced to extend the classic transit compartment model of chemotherapy-induced myelosuppression. The gamma distribution provides an additional shape parameter that is equal to the number of transit compartments if it assumes an integer value.

The objective of this presentation is to demonstrate deterministic identifiability of the distributed delay model given typical data showing the effect of chemotherapy on white blood cells. The analysis has been performed using the delay operator implemented in Phoenix 8. By attending this webinar, you can learn how the Phoenix delay operator can provide the following benefits:

  • Eliminate the need to add complex lines of code for each delay differential equation
  • Simplify modeling delayed outcomes
  • Avoid inefficient workarounds and approximations

About Our Speaker

Dr. Wojciech Krzyzanski is an Associate Professor of Pharmaceutical Sciences at the University at Buffalo, State University of New York (UB). Dr. Krzyzanski holds a PhD in applied mathematics and a MA in pharmacology. His interests include the modeling of pharmacokinetics and pharmacodynamics of hematopoietic growth factors, the model-based development of optimal dosing regimens for chemotherapy-induced cytotoxicities, particularly myelosuppression, the pharmacometric analysis of properties of various types of indirect response models, and the evolution of target-mediated PK/PD models.


Using Virtual Twin Technology to Predict Drug Exposure in Individual Patients

Speaker(s): Tom Polasek
Date: March 8, 2018
Time: 9 am EDT
Duration: 1 hour

The “one-size-fits-all” approach to healthcare is becoming a thing of the past. The new paradigm of precision medicine aims at delivering the right treatment at the right time to the right patients. An integral part of precision medicine is administration of a precise dose, which is a critical step in the larger mission to deliver personalized healthcare. Precision dosing will provide patients with the most efficacious medications with minimum probability of adverse events.

Modeling and simulation techniques for drug development include both top-down (pharmacometrics) and bottom-up (PBPK models) approaches. While these approaches have had success in drug research and development, they have yet to become a regular tool for “point-of-care” clinical decisions. The transformation of health care from “one-size-fits-all” to a targeted approach utilizing information about an individual patient’s genetics and lifestyle continues to accelerate as the US FDA more regularly and rapidly approves new personalized medicines.

Virtual Twin™ technology will be an important step towards making this vision a reality. The idea is to match the characteristics of a real patient with his or her virtual twin to predict the optimal dose. This matching would happen at several levels:

  • Age, weight, height, sex, and ethnicity
  • Current drug dosage and co-medications
  • Activity of metabolic enzymes and transporters
  • Level of organ function

Realization of this technology would allow clinicians to first try different drug doses, schedules, and combinations in the virtual twin to determine an optimal dosing regimen for the patient.

In this webinar, Dr. Tom Polasek, a clinical pharmacologist at Certara Strategic Consulting, will explain how he used the Simcyp Simulator to predict olanzapine exposure in individual patients. By attending this webinar you will learn how PBPK modeling and simulation technology can be re-purposed to support model-informed precision dosing.

About Our Speaker

Dr. Tom Polasek received his BPharm (Hons) degree from the University of South Australia and BSc, MD and PhD degrees from Flinders University. He has 20 years of experience in numerous roles across the Australian tertiary education and healthcare sectors, including as a scientist, pharmacist, doctor and academic. Tom’s research interests include all aspects of clinical pharmacology, but are particularly focused on the clinical application of modeling and simulation approaches to improve the quality use of medicines. Tom is the author/co-author of more than 80 peer-reviewed articles and conference presentations. In addition to his role as Clinical Pharmacologist at d3 Medicine (a Certara Company), Tom is a Medical Officer at CMAX Clinical Research Pty Ltd and Senior Lecturer in Clinical Pharmacology at Flinders University in Adelaide. In his spare time Tom enjoys listening to and playing music, coaching his son’s cricket team, watching his daughter dance, and trying to keep fit.

 


Pediatric PBPK/PD Models for Drug Development and Clinical Use—What’s Here, What’s Near?

Speaker(s): Alexander Vinks
Date: March 29, 2018
Time: 11 am EDT
Duration: 1 hour

The application of physiologically-based pharmacokinetic (PBPK) modeling to quantitatively describe drug disposition and effects in neonates, infants and children is increasingly being used in both industry and academia. Pediatric PBPK allows optimal use of all available information for both efficient study designs and prediction of drug exposure. This “systems pharmacology” approach conforms with the FDA Critical Path Initiative’s recommendation to apply innovative computational techniques to integrate the effects of pediatric physiological changes to describe and predict drug disposition as associated with response to therapy and adverse events.1

In 2012, the FDA’s Pharmaceutical Science and Clinical Pharmacology Advisory Committee voted unanimously in support of extending the use of PBPK modeling for pediatric drug development. Notably, several committee members emphasized the need for more data (ie, ontogeny of transporters) and prospective evaluation and validation of the models with observed data (www.fda.gov, March 14, 2012, Gaylord National Resort & Convention Center, National Harbor, MD).

Pediatric PBPK models integrate drug information with the unique “physiologically-based” developmental context and allow capturing the effects of changes of such aspects as body size and composition, tissue blood flows, and biochemical features of the developing body.

Most recently, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) through its Best Pharmaceuticals for Children Act (BPCA) activities has hosted several roundtable discussions that have focused on age-dependent changes in pharmacodynamics (PD). One recommendation from these expert meetings was the formation of a working group focusing on the application of pediatric pharmacometrics and the development of PBPK models that include a PD component. This may provide an attractive starting point for the development of pediatric PBPK/PD platforms able to simultaneously evaluate age-specific developmental changes in drug disposition and response through the linking of drug exposure with pharmacodynamic target effect(s).

This presentation will cover PBPK and pediatric precision medicine linking model-based predictions with clinical observations. By attending this webinar, you will learn the following:

  • How growth and maturation processes are predictive of pediatric drug disposition and effects
  • How PBPK modeling & pharmacometrics can bridge and facilitate the design of informative pediatric clinical studies
  • How modeling and simulation and PBPK can improve our mechanistic understanding of drug disposition in neonates and infants

References

[1] Vinks AA, Emoto C, & Fukuda T. (2015). Modeling and simulation in pediatric drug therapy: Application of pharmacometrics to define the right dose for children. Clin Pharmacol Ther, 98, 298-308, DOI:10.1002/cpt.169


About Our Speaker

Dr. Alexander Vinks is the Cincinnati Children’s Research Foundation Endowed Chair in Clinical Pharmacology and Professor of Pediatrics and Pharmacology at the University of Cincinnati, College of Medicine. He is the Director of the Division of Clinical Pharmacology and serves as the Program Director of the NIH Postdoctoral Training Program (T32) in Pediatric Clinical Pharmacology at Cincinnati Children’s Hospital Medical Center. He is the director of Pharmacy Research, co-director of Cincinnati Children’s Genetic Pharmacology Program, and he directs a multidisciplinary Pediatric Pharmacometrics Center of Excellence.

Dr. Vinks received his academic training at Leiden University and the University of Toronto. He is a Fellow of the American College of Clinical Pharmacology. He is past president of the International Association of Therapeutic Drug Monitoring and Clinical Toxicology. Dr. Vinks is Associate Editor for Clinical Pharmacology & Therapeutics and serves on several editorial boards, including CPT-Pharmacometrics and Systems Pharmacology.

His research interests include systems pharmacology, physiologically-based pharmacokinetics (PBPK), pharmacogenetics/genomics, pharmacokinetic-pharmacodynamic (PK/PD) modeling, and the application of population and simulation methods to inform pediatric clinical trial design and therapeutic drug management through the implementation of model-based precision dosing.


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