Application of Pharmacokinetic Prediction Platforms in the Design of Optimized Anti-Cancer Drugs

Area of Science:

• Oncology
• Pharmacology
• Computational Chemistry

Background:

• Cancer is a leading cause of death, with novel drugs showing increased toxicity.
• Pharmacokinetic (PK) optimization and reduced off-target interactions are crucial for mitigating drug toxicities.
• Early implementation of PK prediction tools is needed in drug discovery.

Purpose of the Study:

• To evaluate the utility of publicly available PK prediction platforms in early-stage drug discovery.
• To optimize mitogen activated extracellular signal-related kinase kinase 1 (MEK1) inhibitors using PK prediction tools.
• To identify MEK1 inhibitors with retained activity, optimized PK properties, and reduced toxicity.

Main Methods:

• Utilized several PK prediction platforms, including pkCSM, SuperCypsPred, Pred-hERG, SEA, and SwissADME.
• Applied PK prediction tools for the optimization of MEK1 inhibitors.
• Screened compounds to select those with reduced toxicity and attrition risk.

Main Results:

• Identified MEK1 inhibitors with retained anti-cancer activity.
• Achieved optimized predictive PK properties for selected MEK1 inhibitors.
• Ensured identified MEK1 inhibitors were devoid of hERG inhibition, a key safety concern.

Conclusions:

• Publicly available PK prediction platforms are valuable for early-stage drug discovery.
• These tools facilitate the design of safer oncology drugs with improved PK profiles.
• Integrating PK prediction aids in selecting compounds with reduced toxicity and attrition risk.