How Can You Get the Most out of Phoenix 8.0?

Speaker(s): Nathan Teuscher
Date: November 1, 2017
Time: 11 am EDT
Duration: 1 hour

As the gold standard for pharmacokinetic and pharmacodynamics (PK/PD) software, Phoenix 8.0 continues to innovate and deliver new features to WinNonlin, NLME, and its workbench to reduce errors, save time, and support the regulatory review process. Phoenix 8.0 is a significant update to the most advanced PK/PD software used worldwide by over 6,000 researchers, nearly 2,000 institutions, and multiple regulatory agencies including 11 divisions of the FDA to analyze PK/PD data.

  • Enhanced NCA Engine with Phoenix WinNonlin 8.0: Phoenix WinNonlin 8.0 provides automated calculation of over a dozen new parameters for plasma and urine which will save time by eliminating manual work and reduce errors with non-compartmental analysis. Users can also define their own parameters such as compute concentrations at any time point and arithmetic combinations of any NCA parameter. Furthermore, users can set strict criteria for the calculation of the terminal slope in NCA to ensure compliance with organizational policies and procedures.
  • All New WinNonlin 8.0 Validation Suite Available at Launch: The new validation suite is integrated into Phoenix 8.0 so there is no need to wait to validate or to install a separate application. The average run time for the new validation suite takes less than 30 minutes, significantly less than other validation options which can take days. Validation results are saved and immediately available in Phoenix 8.0 for easy reference.
  • Faster Model Runs with Phoenix NLME 8.0: Phoenix NLME 8.0 offers parallelization to run on powerful remote compute platforms, reducing run times from days to minutes. Phoenix NLME 8.0 also includes the distributed delay function to model delayed outcomes in therapeutic areas such as oncology, diabetes and arthritis.
  • New Features in the Phoenix Workbench: For quality control, users can now lock Phoenix workflows to prevent any changes and load and save object settings with one click.

In this webinar, Dr. Nathan Teuscher will demonstrate how to get the most out of Phoenix 8.0’s new features, including how to automatically calculate new NCA parameters, set up parallelization of Phoenix NLME runs, and leverage the new validation suite to get up and running in no time.

About Our Speaker

Nathan Teuscher is the Vice President of Pharmacometric Solutions at Certara. He is a leader in the pharmaceutical industry as a scientist, consultant, and teacher. Nathan has been teaching and training for over 15 years in the pharmaceutical industry, providing lectures in general pharmacokinetics to non-scientists, and specialized training in population pharmacokinetics and drug development to industry experts.

 


Best Practices in Clinical Pharmacology Gap Analysis

Speaker(s): Julie Bullock
Date: November 14, 2017
Time: 11 am EDT
Duration: 1 hour

Submitting your New Drug Application (NDA) to the US Food and Drug Administration (FDA) is the ultimate test of a drug program. Are you confident that you’ll have robust answers to the 40 different questions that the agency will ask about your clinical pharmacology data package at the time of a NDA submission? If the thought gives you “pre-test jitters,” you might want to invest in a clinical pharmacology gap analysis—a tool that can help you evaluate and address any potential gaps in your program before the FDA does.

The field of clinical pharmacology can help stakeholders address these challenges and improve decision-making at critical milestones, whether early in proof-of-concept phases (pre-clinical through 2a) or in the later stages where a more robust risk and efficacy profile is established (2b through 3). The tools, methods, and frameworks (eg, mechanistic or quantitative) of clinical pharmacology span distinct sub-specialties and can significantly impact these pre-clinical and clinical phases.

Attend this webinar to learn from Dr. Julie Bullock, Senior Director of Consulting Services at Certara, how gap analysis can help you ensure that your development program will contain all the elements needed to satisfy regulators and investors during all phases of drug development from IND to NDA. By attending this webinar, you will learn the following:

  • What questions the agency will ask about your clinical pharmacology data package at the time of a NDA submission
  • How gap analysis can help you develop a clinical pharmacology development strategy that covers all relevant domains
  • What data to gather and when to gather it to enhance decision-making during development
  • How a clear clinical pharmacology plan can assist in negotiations with regulators and investors during IND development
  • What quantitative analyses (pharmacometrics and other model-informed drug development technologies) can be leveraged to diminish dedicated study needs and accelerate your path to drug approval

About Our Speaker

Dr. Bullock has over 10 years of drug development experience within the FDA. Dr. Bullock’s past appointments include Clinical Pharmacology Team Leader and Senior Clinical Pharmacology Reviewer (FDA). Her regulatory experience was focused in the therapeutic areas of hematology/oncology and coagulation. She has unique insight in pediatric development, PK/PD approaches for biosimilar products, oncology dose finding strategy, and streamlining development for breakthrough therapies and accelerated approval. Dr. Bullock has contributed to over 14 new molecular entity approvals during her 10 year FDA career.


What’s New in the Simcyp Simulator v17?

Speaker(s): Nikunjkumar Patel, Matthew Harwood, Oliver Hatley
Date: December 5, 2017
Time: 10 am EST
Duration: 1 hour

Conducting clinical trials incurs immense costs. Thus, technologies that inform and complement clinical trials represent a sea change in drug development. Sponsors and regulatory agencies routinely use physiologically-based pharmacokinetic (PBPK) modeling and simulation to assist in dose selection and inform product labeling.

PBPK models describe the behavior of drugs in different body tissues. Depending on the route of administration, the course of the drug can be tracked through the blood and tissues. Each tissue is considered to be a physiological compartment. The concentration of the drug in each compartment is determined by combining systems data, drug data, and trial design information. The systems data includes demographic, physiological, and biochemical data for the individuals in the virtual study population. The drug data consists of its physicochemical properties, its binding characteristics, and information on its metabolism and solubility. The trial design information comprises the dose, administration route, dosing schedule, and co-administered drugs.

The Simcyp® Simulator links in vitro data to in vivo ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetic/pharmacodynamic (PK/PD) outcomes to help explore potential clinical complexities prior to human studies and support decision-making in drug development.

Join this webinar with Nikunjkumar Patel, Oliver Hatley, and Matthew Harwood to learn how the latest updates in the Simcyp Simulator v17 will help provide insights that support developing safer, more effective medications. These enhancements include:

  • Expansion of Populations Library: Cancer patients differ from healthy people in terms of their demographics and their abundances of blood plasma binding proteins and hepatic transporters. These changes can mean that the pharmacokinetics of a drug may be altered in this population. The Simcyp Simulator v17 includes a new virtual cancer population as a generic population template for modeling PBPK in oncology.
  • Multi-phase Multi-layer (MPML) Mechanistic Dermal (MechDermA) Model: The ability to estimate systemic exposure from dermal absorption is essential in developing new dermatological medications or assessing the toxicological liability of commercially-used chemicals. The previous dermal model in the Simcyp Simulator was based on the skin physiology of healthy male and female Caucasian subjects. As part of a multi-year FDA grant, the model has been enhanced to include pediatric and geriatric populations, additional ethnic groups, and specific skin diseases such as psoriasis. All major topical and transdermal delivery systems can be simulated. The model also allows identification of clinically relevant critical product quality attributes which can aid product specification. In addition, a vehicle evaporation model has been added to the MPML― the MechDermA model― to account for the effect of vehicle evaporation on dermal drug absorption from topical formulations.
  • Expansion of Gut Transporters and IVIVE Techniques in the ADAM/M-ADAM Models: Drug transporters play a vital role in governing drug concentrations in the blood, liver, brain, intestine, lung, and kidney. Transporter protein-mediated drug-drug interactions (DDIs) can cause loss of drug effectiveness and toxicity. To gain greater insights into the role of transporters in PK/PD and toxicity, an additional 14 gut transporters have been added to the Advanced Dissolution, Absorption and Metabolism (ADAM) and Multi-layer ADAM (M-ADAM) models with the ability to scale to in vivo via both relative and absolute transporter abundances utilizing the appropriate intestinal membrane based scaling factors.

About Our Speakers

Nikunjkumar Patel is a senior research scientist in Certara’s modeling and simulation group where he is heavily involved in oral and dermal absorption modeling projects and is a member of the Cardiac Safety Simulator development team. He joined Certara in August 2011 and led the development of the physiologically based IVIVC (PB-IVIVC) module of the Simcyp Simulator and the Pharmaceutics module of the SIVA (Simcyp In Vitro (data) Analysis) platform. Before joining Certara, he spent three years at the life science innovation labs of Tata Consultancy Services as a research scientist mainly working on pharmacokinetic/pharmacodynamic modelling and QSAR development for various ADMET properties. During his graduate studies, he used computer aided drug design (CADD) and molecular modeling to identify safe and potent novel anti-diabetic ligands.

Matthew Harwood is a senior research scientist at Certara. He obtained his Bachelor’s and Master’s degrees in Physiology and Human Nutrition from The University of Sheffield and his PhD from The University of Manchester. His early career involved undertaking nutritional and enterocyte transport research in Cystic Fibrosis at Sheffield Children’s Hospital. Since 2007, Matthew has been working for Certara and is principally involved in the development of human PBPK models developing models for incorporating enzymes and membrane transport proteins, and pre-clinical transporter protein knock-out PBPK models. He has a keen interest in ADME transporter proteomics with respect to translation into IVIVE-PBPK strategies.

Oliver Hatley is a senior research scientist who has been working at Certara since 2013. He obtained his PhD investigating in vitro-in vivo extrapolation of intestinal metabolism from the Centre for Applied Pharmacokinetic Research (CAPKR) at the University of Manchester. Oliver is part of the translational sciences in DMPK group within Simcyp and has led development of the esterase organ and blood in vitro-in vivo scaling strategies. He is also involved in the development of special populations within the Simcyp Population-based Simulator.


How to Perform Level C IVIVC in Phoenix

Speaker(s): Jean-Michel Cardot
Date: December 13, 2017
Time: 11am EDT
Duration: 1 hour

In vitro in vivo correlation (IVIVC) is built on the premise that the in vitro dissolution characteristics of a drug can serve as a surrogate for a bioequivalance study. This type of analysis is attractive to sponsors because dissolution assays are cheaper and faster to perform than clinical testing. It also provides reassurance that a positive benefit/risk balance for patients is maintained throughout the life of a drug. IVIVC encompasses levels A, B, and C.

Level C correlation relates one dissolution time point (t50%, t90%, dissolution observed at 1h, etc.) to one mean pharmacokinetic parameter such as AUC (the area under the concentration-time curve), Tmax (the time after administration of a drug when the maximum plasma concentration is reached) or Cmax (peak concentration). Only a partial relationship between absorption and dissolution is established since it does not reflect the complete shape of plasma drug concentration time curve, which is the critical factor that defines the performance of a drug product.

Due to its limitations, the usefulness of a Level C correlation is restricted in predicting in vivo drug performance. In early formulation development, Level C correlations can help select pilot formulations. Waiver of an in vivo bioequivalence study (biowaiver) is generally not possible.

In contrast to level C, Multiple Level C correlations refer to the relationship between more than one pharmacokinetic parameter of interest (Cmax, AUC, or any other suitable parameters) and the amount of drug dissolved at several time points in the dissolution profile. Multiple Level C correlations are more powerful than a single level C as it could predict, for example, the two parameters of bioavailability rate (Cmax) and extent (AUC). In those conditions, it may be used to justify a biowaiver provided that the correlation has been established over the relevant dissolution point with all the bioavailability parameters of interest. A multiple Level C correlation should be based on at least three formulations and, if possible, on all the bioavailability parameters: AUC and Cmax.

The development of a level A correlation should also be possible when multiple Level C correlations are achieved for all relevant pharmacokinetic parameters describing rate and extent such that the effect on the in vivo performance of any change in dissolution can be assessed. However, this Level A correlation is not always possible even when a multiple level C exists, for example, when the drug of interest is a metabolite formed pre-systemically or during the elimination processes when administered as a prodrug. In this case, the multiple level C could be seen as the best possible achievable correlation.

Join this webinar with Professor Jean-Michel Cardot to learn how to perform Level C IVIVC using Phoenix. By attending this webinar, you will learn the following:

  • How to calculate pharmacokinetic parameters via the non-compartmental analysis (NCA) module
  • How to calculate dissolution parameters via the dissolution module
  • How to link the in vivo pharmacokinetic parameters and in vitro dissolution parameters via the linear relationship module
  • How to calculate dissolution limits

About Our Speaker

Jean-Michel Cardot is a professor and head of the Department of Biopharmaceutics and Pharmaceutical Technology at the Auvergne University in France. Prior to coming to Auvergne University, he worked in the pharmaceutical industry for 15 years. Prof. Cardot earned degrees in pharmacy (PharmD), a Masters in Bio-pharmaceutical, Statistical sciences and Pharmacokinetics, and a doctorate in pharmaceutical sciences from Auvergne University. His research interests include biopharmaceutical development of drugs, in vitro dissolution, and in vivo bioequivalence and in vitro-in vivo correlation.


Learn More LinkedIn Twitter Facebook Email