Quantitative, Translational and ADME Sciences (QTAS) - Multiple Opportunities (Pipeline)

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

The Quantitative, Translational and ADME Sciences (QTAS) organization plays a vital role in increasing the probability of success of AbbVie’s pipeline by supporting Early Discovery and continuing through all phases of clinical development. QTAS scientists mechanistically investigate how drug molecules are absorbed, distributed, excreted, metabolized, and transported across the body to predict duration and intensity of exposure and pharmacological action of drug candidates in humans. QTAS is responsible for:

• Quantitative Translational Mechanistic Modeling Group
• ADME Sciences
• Bioanalysis
• Project Support Model

Locations: Lake County IL, Worcester MA, Bay Area and Irvine CA, Ludwigshafen Germany

There are Key Roles that work together to mechanistically understand drug behavior, generate critical data, provide scientific guidance throughout the drug development lifecycle.  See Below for more information regarding each pillar and criteria for each role.

Quantitative Translational Mechanistic Modeling                                                                                                                      

Translational modeling utilizes mathematical equations with parameters informed by relevant in silico, in vitro, nonclinical in vivo and clinical data to predict human pharmacokinetics (PK) and pharmacodynamics (PD) for new drugs. Human PK is projected using methods including but not limited to allometric scaling and physiologically based pharmacokinetic (PBPK) modelling.  These models can be used to predict, e.g., tissue exposures, drug-drug interactions (DDI), food effect, ethnic or genetic difference in pharmacokinetics, and integrated into PK/PD models to project safe and efficacious human doses. Empirical PK/PD approaches typically relate systemic drug exposure directly to efficacy, while mechanistic models may also incorporate elements of tissue/cellular distribution, target engagement and downstream pharmacology. These translational modeling approaches are being rapidly adopted in Discovery at early stages to inform target selection, modality selection and property optimization strategies, and at later stages to project efficacious dosage regimens and rationally design first-in-human clinical trials.

• Quantitative Translational Mechanistic Modelers
  • Develops and applies mathematical models to predict human pharmacokinetics (PK) and pharmacodynamics (PD) for new drugs.
  • Uses in silico, in vitro, nonclinical, and clinical data to inform model parameters.
  • Projects tissue exposures, drug-drug interactions, food effects, and genetic differences in PK.
  • Supports target selection, property optimization, and dosage regimen design for clinical trials.

ADME Sciences                                                                   ��                                                                                                             

ADME refers to the study of the absorption, distribution, metabolism, and excretion of drugs using cell cultures, tissue, preclinical species, or humans. The four criteria influence the drug levels and kinetics of drug exposure to tissues and hence influence the performance and pharmacological activity of the compound as a drug.  ADME assays help identify drug candidates that are more likely to be successful in clinical trials and advancing drug candidates that are more efficacious, less toxic, and easier to administer.

 ADME studies throughout drug discovery and development provide critical data required to determine the safety and efficacy of a potential new drug. The information provides insights on how a drug is absorbed, distributed, metabolized, excreted from the body, and pharmacokinetic behavior to drive molecules to the market.  The data generated is used to influence drug design/chemistry, understand risks associated with drugs (drug-drug interactions, reactive metabolites, safety coverage, active metabolites, toxicological activity, clearance mechanism), provide data required for regulatory filing, and provide scientific advice to project teams.

• ADME Scientists