Preventing Adverse Patient Responses to Cancer Chemotherapeutics

Abstract

The purpose of this project is to prevent adverse patient responses to the cancer drug irinotecan by analyzing the gut microbiomes of patients. The scope of this project is to study irinotecan metabolism and the microbiome over time using fecal samples from healthy individuals and metastatic colorectal cancer patients. We have several major findings from the past year of work. We are happy to report a number of significant results for this year. We have successfully collected longitudinal samples for four colorectal cancer patients. We were invited to write a review for Annual Reviews in Pharmacology and Toxicology about microbiome contributions to adverse events and we discuss the potential for microbiome interventions to improve drug and treatment safety and efficacy in colorectal cancer (Khan, Hauptman, and Kelly, Ann Rev Pharm Tox, in press). We wrote commentaries on harnessing the microbiome to improve drug therapy (Kelly, Clin Pharmacol Ther, 2019) and on microbial metabolism of L-dopa (Hitchings and Kelly, Cell Metab, 2019). We developed a novel computational approach to identify metastable states and state transitions in microbiome data that are linked to patient outcomes (Chang, VanInsberghe, and Kelly, npj Biofilms and Microbiomes, provisionally accepted). To better predict the likelihood of a patient suffering an adverse event based solely on his or her microbiome, we developed, tested, and validated three machine learning approaches to predict clinical outcomes based on microbiome data (Khan and Kelly, Pac Symp Biocomput, 2020). Each publication acknowledges the DoD.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2020
Accession Number
AD1105441

Entities

People

  • Libusha Kelly

Organizations

  • Albert Einstein College of Medicine

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Cancer
  • Computational Science
  • Data Analysis
  • Drug Therapy
  • Engineering
  • Gut Microbiome
  • Information Science
  • Machine Learning
  • Medical Personnel
  • Microorganisms
  • Pharmacology
  • Standards
  • Statistical Analysis
  • Statistical Tests
  • Therapy
  • Toxicology

Fields of Study

  • Biology
  • Medicine

Readers

  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.
  • Oncology
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • Biotechnology