A method for systematically ranking therapeutic drug candidates using multiple uncertain screening criteria

Area of Science:

• Biotechnology
• Computational Biology
• Pharmacology

Background:

• Drug development screening often yields ambiguous results, complicating lead candidate selection.
• Multiple screening criteria for antibody therapeutics present a complex decision-making challenge.

Purpose of the Study:

• To apply multiple criteria decision making (MCDM) methods for ranking antibody therapeutics.
• To introduce novel metrics, Retention Probability and Topness, for robust candidate ranking and confidence assessment.
• To develop a systematic approach for identifying lead antibody therapeutics for Alzheimer's disease.

Main Methods:

• Employed the SMAA-TOPSIS method to rank antibodies based on up to eight weighted screening criteria.
• Assessed the robustness of rankings against uncertainties in screening measurements and criterion weights.
• Proposed and utilized Retention Probability and Topness for evaluating ranking confidence.

Main Results:

• The MCDM method successfully identified true positives and negatives in synthetic data.
• Rankings correlated well with the clinical status of antibodies (approved vs. in trials).
• The method facilitates ranking analysis using antibody developability profiles.

Conclusions:

• The proposed MCDM approach offers a systematic way to screen antibody therapeutics, especially with complex multi-variate data.
• This method can expose additional potential leads, increasing the likelihood of success in clinical trials.
• A webserver is available for applying this method to user-generated data.