Submit a Manuscript to the Journal

Geriatric Pharmacology

For an Article Collection on

Model-Informed Drug Development for Geriatric Populations

Manuscript deadline

Article Collection Guest Advisor(s)

Dr. Victor Crentsil, Johns Hopkins University School of Medicine
[email protected]

Journal information

Submit an article to Geriatric PharmacologyView Geriatric Pharmacology on Taylor & Francis OnlineRead the Instructions for Authors on Geriatric Pharmacology

Model-Informed Drug Development for Geriatric Populations

Model-Informed Drug Development (MIDD) is a methodologic paradigm that leverages mathematical or computational modelling and simulation. It combines non-clinical and clinical data and other existing knowledge to generate evidence that facilitates decision-making during drug development. It is transforming approaches to evidence synthesis and integration, as well as provision of actionable insights for contemporary development of therapeutic products.

Although the modifying effects of aging on drug pharmacokinetics (PK) and pharmacodynamics (PD) are widely known, older adults are under-represented in drug development programs, which has resulted in evidence gaps in geriatric therapeutics. Geriatric under-representation in drug development programs is multifactorial; however, MIDD, through its utility for optimizing study design, dose selection, benefit-risk evaluation, increasing efficiency, decreasing development costs and time, and minimizing risk, is likely to accelerate the closure of evidence gaps in geriatric therapeutics than traditional methods. MIDD is enjoying growing endorsement of regulatory authorities and its risk minimizing attributes makes it advantageous for high-risk populations. Consequently, highlighting MIDD’s achievements in older populations is likely to facilitate its widespread consideration for advancing geriatric pharmacology. Geriatric populations are increasing worldwide; therefore, the earlier MIDD is promoted and implemented in older populations, the sooner the benefits will be realized globally.

Subtopics of interest may include but not limited to:

  1. Applications of MIDD in Geriatric Populations
  2. Advanced Aging and its Impact on Model Development, Evaluation, and Implementation
  3. Potential Challenges in Using MIDD to Document Evidence in Geriatric Populations
  4. Leveraging Artificial Intelligence/Machine Learning to Facilitate or Enhance Model Development and Decision-Making in Geriatric Therapeutics
  5. Utilizing MIDD to Accelerate Therapeutic Development for Alzheimer’s and Other Dementias
  6. MIDD for Geriatric Frailty and other Syndromes Prevalent in Older Populations
  7. MIDD for Enriching Geriatric Subsection of Labelling of Therapeutics
  8. Model-Based Meta-Analysis for Benefit-Risk Assessments of Therapeutics in Geriatric Populations
  9. Disease Progression Models for Development of Drugs Predominantly Used in Geriatric Populations
  10. Engaging with Regulatory Authorities to Facilitate Acceptance of MIDD for Therapeutic Products Predominantly Used by Geriatric Populations

Keywords:

  • Aging
  • Geriatrics
  • Drug Development
  • Modelling and Simulation
  • Quantitative Pharmacology

All manuscripts submitted to this Article Collection will undergo desk assessment and peer review if they can pass the desk assessments as part of our standard editorial process; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

The deadline for submitting manuscripts is 31 October 2026.

Please contact Catherine Teng at [email protected]  with any queries and discount codes regarding this Article Collection.

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All manuscripts submitted to this Article Collection will undergo desk assessment and peer-review as part of our standard editorial process. Guest Advisors for this Collection will not be involved in peer-reviewing manuscripts unless they are an existing member of the Editorial Board. Please review the journal Aims and Scope and author submission instructions prior to submitting a manuscript.