Drug Translation

New drug development can be divided into two phases: (1) discovery, which tends to be chemistry-centric, and (2) translation, which focuses on animal and clinical therapeutic responses. The figure to the right illustrates how few new drugs make the journey from initial drug lead discovery to final approval in humans as a “Drug Discovery Funnel.” During the first discovery phase, only 4% of initial drug leads progress to early animal work. SMDA’s Drug Discovery working group aims to increase success rates in early drug screening and optimization by leveraging large-scale drug datasets and modifying in vitro validation assays to improve clinical validity.

In the second translational phase, a mere 0.2% of drug leads are approved for use in humans. SMDA’s Translational working group aims to increase success rates in clinical translation through modeling drug response (PK-PD) and better matching drug leads to the patient populations who will benefit from them (diagnostics).

Overall, these two working groups strive to fulfill a major strategic goal of SMDA and POH: to better integrate research across the drug development process to increase success rates and reduce drug development costs. This page provides an overview of SMDA’s Translational working group’s current priority areas and shared resources.

 

Translational Drug Data: patient-measurements vs drug-response

A key aspect of translational and clinical drug research is its focus on patient- or animal-specific physiological measurements. Diseases are often diagnosed by quantifying symptoms through various metrics such as weight, heart rate, and blood pressure. These measurements guide clinical practice by identifying diseases and evaluating their severity.

In addition to patient-specific data, molecular biomarkers from patient samples (e.g., blood, urine, biopsies) are used to more precisely define disease states and identify appropriate drugs for individual patients. Blood and biopsy samples allow for monitoring drug concentrations, evaluating immune cell populations, and assessing disease populations such as bacteria and cancer. Beyond cell type frequencies, we can now analyze genomic variants, gene expression, and protein and metabolite concentrations within both immune and disease cells (see figure to the right). This comprehensive analysis enables us to take a detailed census of the disease “battlefield” during anti-cancer, anti-viral, or anti-bacterial immune responses.

The SMDA PK-PD working group focuses on utilizing these various physiological and molecular measurements to better understand drug responses in patients across both veterinary and human medicine.

 

 

 

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Tactical Priorities Recruit Workforce:  with interests in molecular diagnostics and PK-PD modeling Train Workforce:  to use emerging tools of PK-PD modeling data-science machine-learning Build Collaborations:  between relevant disciplines clinicians scientists Automate:   laborious and iterative processes to: increase efficiency of research increase access to advanced computational methods Map Community Datasets: cancer genomics infectious disease genomics immunology

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Strategic Priorities Molecular Diagnostics: using biomarkers to predict drug-response patient survivial PK-PD modeling: to better understand patient variability in drug-response in vivo drug response clincial drug drug interactions Digital Teaching Assistants: ChatBots built off of literature underlying traditional coursework Textbook Mobile Apps:   mobile apps based on data and equations underlying current textbooks … …

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Current Members

• Eugene Douglass
• Jonathan Mochel
• Karin Allenspach
• Silvia Carnaccini
• Janet Grimes
• Robin Southwood

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Software Tools

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Cleaned Datasets

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Key Performance Indicators: summary statistics

 

KPI Category	KPI	Current Value
Research and Publications	Number of Published Papers
	Impact Factor of Journals
	Citations
	Conference Presentations
Collaboration and Engagement	Interdisciplinary Projects
	External Collaborations
	Collaborative Publications	%
	Workshops and Seminars
Funding and Grants	Research Grants Received	$
	Grant Applications Submitted
	Grant Success Rate	%
Data and Tools	Datasets Published
	Software Tools Developed
	Tool Adoption	downloads
Training and Development	Students Supervised
	Training Programs
	Skill Development	certificates
Impact and Outreach	Societal Impact
	Media Mentions
	Public Engagement	events
Operational Efficiency	Project Completion Rate
	Data Management Practices	% compliance
	Resource Utilization	% efficiency
Innovation and Excellence	Awards and Recognitions
	Innovative Solutions	breakthroughs
Feedback and Improvement	Stakeholder Feedback	satisfaction
	Continuous Improvement	# iterations

 

 

 

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Key Performance Indicators: specific items list

 

KPI List: password protected

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