Clinical Pharmacology Intern – Oncology
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Job Location:
San Diego, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
4 hours ago
Vacancies:
1 Vacancy
Job Summary
Position Details:
Our client a world-leading Pharmaceutical Company in San Diego CA is currently looking for an Clinical Pharmacology Intern Oncology to join their expanding team.
Job Title: Clinical Pharmacology Intern Oncology
Duration: 3 months contract extendable up to 48 months
Location: San Diego CA
Note:
The client has the right-to-hire you as a permanent employee at any time during or after the end of the contract.
You may participate in the company group medical insurance plan
Job Description:
Our client a world-leading Pharmaceutical Company in San Diego CA is currently looking for an Clinical Pharmacology Intern Oncology to join their expanding team.
Job Title: Clinical Pharmacology Intern Oncology
Duration: 3 months contract extendable up to 48 months
Location: San Diego CA
Note:
The client has the right-to-hire you as a permanent employee at any time during or after the end of the contract.
You may participate in the company group medical insurance plan
Job Description:
Internship Overview:
We are seeking a highly motivated graduate level intern to support translational PK-PD modeling efforts focused on antibody drug conjugates (ADCs) containing the payload.
This internship will investigate the mechanistic and translational drivers of variability in preclinical tumor static concentration (TSC) estimates across ADCs and evaluate modeling strategies to improve interpretation of preclinical efficacy signals.
The intern will work closely with clinical pharmacology and translational modeling scientists to inform early ADC candidate selection and development decisions.
We are seeking a highly motivated graduate level intern to support translational PK-PD modeling efforts focused on antibody drug conjugates (ADCs) containing the payload.
This internship will investigate the mechanistic and translational drivers of variability in preclinical tumor static concentration (TSC) estimates across ADCs and evaluate modeling strategies to improve interpretation of preclinical efficacy signals.
The intern will work closely with clinical pharmacology and translational modeling scientists to inform early ADC candidate selection and development decisions.
Project Description:
containing ADCs often demonstrate broadly similar systemic pharmacokinetics and safety profiles across targets; however xenograft PK/PD modeling has revealed substantial variability in preclinical tumor static concentration (TSC) estimates across different ADCs.
Emerging internal and limited clinical observations suggest that ADCs with lower preclinical TSC ranges may demonstrate greater translational promise than those with higher TSC estimates raising questions about the mechanistic drivers and translational relevance of TSC as a preclinical efficacy metric.
containing ADCs often demonstrate broadly similar systemic pharmacokinetics and safety profiles across targets; however xenograft PK/PD modeling has revealed substantial variability in preclinical tumor static concentration (TSC) estimates across different ADCs.
Emerging internal and limited clinical observations suggest that ADCs with lower preclinical TSC ranges may demonstrate greater translational promise than those with higher TSC estimates raising questions about the mechanistic drivers and translational relevance of TSC as a preclinical efficacy metric.
This project will evaluate translational modeling strategies to explain variability in preclinical TSC estimates using exclusively in vitro and in vivo preclinical data.
The intern will integrate xenograft efficacy models with mechanistically relevant ADC specific attributes and explore alternative or augmented modeling frameworks to better contextualize TSC across ADCs.
The intern will integrate xenograft efficacy models with mechanistically relevant ADC specific attributes and explore alternative or augmented modeling frameworks to better contextualize TSC across ADCs.
Key Responsibilities:
Curate harmonize and quality check in vitro and in vivo preclinical datasets across multiple ADCs.
Standardize xenograft PK/PD model structures and tumor static concentration (TSC) estimation approaches across compounds.
Perform comparative multivariate and model based analyses to identify key drivers of TSC variability.
Evaluate existing translational PK/PD models and their assumptions across comparable experimental conditions.
Explore alternative mechanistic semi mechanistic or systems informed translational modeling strategies to rationalize observed TSC differences.
Interpret findings in the context of preclinical translational relevance and early ADC decision making.
Prepare a final technical summary and presentation outlining modeling results and recommendations.
Curate harmonize and quality check in vitro and in vivo preclinical datasets across multiple ADCs.
Standardize xenograft PK/PD model structures and tumor static concentration (TSC) estimation approaches across compounds.
Perform comparative multivariate and model based analyses to identify key drivers of TSC variability.
Evaluate existing translational PK/PD models and their assumptions across comparable experimental conditions.
Explore alternative mechanistic semi mechanistic or systems informed translational modeling strategies to rationalize observed TSC differences.
Interpret findings in the context of preclinical translational relevance and early ADC decision making.
Prepare a final technical summary and presentation outlining modeling results and recommendations.
Required Qualifications:
Enrollment in a graduate program (MS PhD PharmD or equivalent) in pharmacometrics clinical pharmacology biomedical engineering biostatistics quantitative biology or a related field.
Strong quantitative and analytical skills with demonstrated interest in translational PK/PD modeling
Experience programming and analyzing data in R (or equivalent statistical software)
Familiarity with PK/PD concepts multivariate analysis and model based data interpretation
Ability to synthesize complex quantitative results and communicate findings clearly to scientific audiences.
Enrollment in a graduate program (MS PhD PharmD or equivalent) in pharmacometrics clinical pharmacology biomedical engineering biostatistics quantitative biology or a related field.
Strong quantitative and analytical skills with demonstrated interest in translational PK/PD modeling
Experience programming and analyzing data in R (or equivalent statistical software)
Familiarity with PK/PD concepts multivariate analysis and model based data interpretation
Ability to synthesize complex quantitative results and communicate findings clearly to scientific audiences.
Preferred Qualifications:
Prior experience with PK/PD or mechanistic modeling particularly in oncology or biologics.
Familiarity with xenograft efficacy modeling and preclinical tumor growth models (PK-TGI).
Exposure to antibody drug conjugates (ADCs) payload pharmacology or target mediated drug disposition.
Experience integrating in vitro and in vivo data to support translational modeling.
Interest in model informed drug development (MIDD) and early portfolio decision making.
Prior experience with PK/PD or mechanistic modeling particularly in oncology or biologics.
Familiarity with xenograft efficacy modeling and preclinical tumor growth models (PK-TGI).
Exposure to antibody drug conjugates (ADCs) payload pharmacology or target mediated drug disposition.
Experience integrating in vitro and in vivo data to support translational modeling.
Interest in model informed drug development (MIDD) and early portfolio decision making.
Learning Outcomes:
By the end of this 3 month internship the intern will gain hands on experience in translational modeling of ADCs understand key drivers of preclinical efficacy metrics such as TSC and contribute to best practice recommendations for interpreting preclinical PK/PD data to support early oncology development decisions.
By the end of this 3 month internship the intern will gain hands on experience in translational modeling of ADCs understand key drivers of preclinical efficacy metrics such as TSC and contribute to best practice recommendations for interpreting preclinical PK/PD data to support early oncology development decisions.
