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Quantitative Pharmacology (QP) Network

Network Goals

Scientific Expertise:
  • The Quantitative Pharmacology Network brings together and advances quantitative disciplines across diverse backgrounds, including industry, academia, and regulatory embracing new technology and approaches.
Catalyst for Innovation:
  • The Quantitative Pharmacology Network fosters cutting-edge science in the areas of pharmacometrics and systems pharmacology, and knowledge around biologics, and supports (pre-competitive) collaborative efforts in these areas.
Influence and Impact:
  • The Quantitative Pharmacology Network is the “go to” voice for the application of Quantitative Pharmacology to drug/biologics development and usage, and catalyzes broader awareness, education and adoption of these principles by scientists engaged in the complementary Networks.
Education and Communication:
  • The Quantitative Pharmacology Network offers multiple educational opportunities and encourages submissions to the ASCPT Journal Family as well as open dialogue among editors and members.
Organizational Effectiveness:
  • The Quantitative Pharmacology Network has established a rhythm of business that supports its leadership using the ASCPT calendar as a guide and proactively develop future leaders.

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Upcoming Webinar
 ASCPT Members-Only Webinar Presented by the Quantitative Pharmacology (QP) Network
Title: Application of Machine Learning in Drug Development (Two-Part Series)
Date and Time: 
Part 2 – Friday, November 2, 2018, 2:00 PM EDT
Speaker: Jagdeep Podichetty, PhD
Moderator: Daniela J. Conrado, PhD
Description: Machine learning (ML) is a computationally intensive approach with foundations in statistical theory. Broadly, machine learning integrates data from heterogeneous sources into a model that predicts an outcome. ML is being applied to a range of different areas including self-driving cars, practical speech recognition, effective web search, and drug development.

Part 1 of this webinar series covered acquiring data and formulating problem statement, data curation and visualization, feature selection methods, supervised ML approach, unsupervised ML approach, and real-world applications of ML in drug development.
Visit the Webinar Library to access the recording.

Part 2 of the series will take a deep dive into development of an ML algorithm for predicting improvement in patients with schizophrenia using CATIE clinical trial data. The process of ML step-by-step per the ML workflow will be reviewed as well as some widely-used ML algorithms such as Random Forest, Naïve Bayes and Logistic Regression. Outcome prediction techniques will also be identified.
Registration Link for Part 2https://attendee.gotowebinar.com/register/9114951787697832451

Past Webinars
Quantitative Pharmacology Network Hot Topics Initiative
One of the primary goals of the Quantitative Pharmacology Network is to enhance the educational opportunities for members. Help further the “Hot Topic” communications and raise awareness of new tools and methods developing in quantitative pharmacology, news and views relevant to the discipline, emerging challenges and opportunities, topics of active scientific debate, etc. Suggest your topic via email to agrawal.scientist@gmail.com or hhassan@rx.umaryland.edu today! Current Quantitative Pharmacology Hot Topic Commentaries:
Quantitative Pharmacology Influence & Impact Initiative
Last year, under the visionary leadership of Anne C. Heatherington, PhD, the Impact & Influence Initiative was launched to highlight the impact of Quantitative Pharmacology approaches in clinical pharmacology, translational medicine and therapeutics across the discovery, development, regulation and post-marketing applications. The crowd-sourced compendium of impactful case-examples along with detailed speaker notes provided by the contributors to the Quantitative Pharmacology Network at the ASCPT 2017 Annual Meeting and their contact information are now available:

Sriram Krishnaswam, PhD, FCP

Network Chair

 

Sandra Visser, PhD

Network Vice Chair

 

Karthik Venkatakrishnan, PhD, FCP

Network Past Chair

 

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