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Career Openings


Principal Investigator, Preclinical and Translational Pharmacokinetics and Pharmacodynamics (PKPD)

ID: 687
Department: DMPK
Location: Cambridge Headquarters
# of Openings: 1

Description

Functional area: Drug Metabolism and Pharmacokinetics

 

Position description:

The Drug Metabolism and Pharmacokinetics Department. is searching for a senior PKPD-Translational Modeling Leader. This senior scientist will establish innovative, best practice translational modeling approaches such as PKPD modeling, mechanistic physiological-based PK (PBPK) modeling, and quantitative systems pharmacology (QSP) as a core competency for preclinical research primarily in CV and inflammatory disease areas. In addition to the modeling responsibility, the successful candidate will represent DMPK in Discovery and Development project teams, and provide project support to enable compound progression from discovery to early stages of clinical development.

The successful candidate will be a highly motivated, self-driven and results-oriented person with excellent communication and presentation skills, capable to work both as team player and project driver. A high degree of flexibility in adapting to different projects and excellent networking and relationship-building skills (both internal and external) are required.

 

Responsibilities:

  • Develop PK models for FIH in support of IND filing and provide guidance in (pre)clinical study design, human dose prediction, calculation of therapeutic index, etc.

  • Provide strategic planning, evaluate and implement innovative and quantitative approaches for translational modeling across Discovery and Development projects.

  • Liaise with discovery biology, pharmacology, toxicology, biomarker and clinical scientists to generate data and knowledge supporting the generation and build-up of translational models.

  • Serve as a DMPK representative in Discovery and Development project teams, coordinate and request PK, PKPD, and bio-analytical (BA) studies in a timely manner and communicate protocols, timelines, needs and results back to team, ensuring optimized cycle times.

  • Serve as a liaison with BA CROs to ensure reagent generation, method development and sample analysis timelines are met for GLP studies.

  • Collaborate and build strong internal network with DMPK, discovery and clinical teams to enable knowledge and data integration to guide selection of drug candidates, biomarkers and target validation/engagement studies.

  • Prepare DMPK sections of regulatory documents; provide response to regulatory questions and ensure generation of appropriate reports.

  • Build strong external network in the field of modeling & simulation (e.g. academia, consortia) and strive for external collaboration with consortia, academic partners, CROs.

  • Keep up to date with emerging literature and science in modeling approaches

 

Basic qualifications:

  • Masters, or Doctorate (Ph.D.) in PKPD modeling, Pharmaceutical sciences, or related field, with at least 14+(MS), or 8+(PhD) years of pharmaceutical industry experience. 

  • Hands-on experience in modeling software like Phoenix, WinNonlin, Matlab/SimCYP, GastroPlus, etc. is required.

  • Must have a strong understanding of DMPK, PBPK/PKPD modeling and human dose prediction of small molecules/peptides and able to give clear recommendations for the design of preclinical and clinical studies.

  • Must have strong communication skills and the ability to influence, negotiate and communicate with both internal and external stakeholders.

  • Ability to clearly communicate and influence strategies related to DMPK-BA and translational modeling and simulation is required.

Preferred qualifications:

  • Understanding of drug development and ligand binding assays for measuring biomarkers in preclinical study samples is highly desired

  • Strong understanding of the implication of QSP is desirable.

  • Publication record with PBPK/PKPD modeling examples is a plus.

  • Commitment to builds strong relationships with peers and cross functionally with to enable higher performance.

  • Ability to learn fast, grasps the "essence" of modelling challenges and change course quickly where indicated.

  • Commitment to create a learning environment, be open to suggestions and experimentation for improvement.

  • Passion for data analysis, solving technical problems and applying new technologies to further scientific goals.

  • Embrace the ideas of others, nurture innovation and pair creativity to reality, cost effectiveness and project timelines.


Ironwood is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.





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