適切な投与量を予測することで、薬剤の再利用が成功する可能性がある(Predicting correct dosage may improve success of drug repurposing)

2024-01-12

2024-01-11 ペンシルベニア州立大学(PennState)

◆ペンシルベニア州立大学の研究者によると、既存の承認済み薬を再利用することで、新薬開発における手続きの一部を省略できるが、その効果に関する臨床試験で再利用薬も同様に頻繁に失敗するとのこと。そこで、研究者らは再利用薬の有効な投与量を予測するモデルを開発し、投与量の理解の問題を解決するために、血液中の濃度に基づいて矯正濃度を計算し、臨床での効果を予測できるようにしました。
◆このモデルは再利用薬の効果的な量を見つけるだけでなく、将来的には新しい薬の設計にも使用できる可能性があると述べられています。

<関連情報>

トランスレーショナル研究におけるキナーゼ阻害剤の過剰な濃度が、効果的な薬剤リパーポージングを妨げる Excessive concentrations of kinase inhibitors in translational studies impede effective drug repurposing

Chuan Liu, Scott M. Leighow, Kyle McIlroy, Mengrou Lu, Kady A. Dennis, Kerry Abello, Donovan J. Brown, Connor J. Moore, Anushka Shah, Haider Inam, Victor M. Rivera, Justin R. Pritchard
Cell Reports Medicine Published: October 17, 2023
DOI:https://doi.org/10.1016/j.xcrm.2023.101227

Highlights

•Clinical drug repurposing efforts are prone to failure
•Effective drug exposure accurately predicts inhibitor efficacy across target variants
•A model trained on leukemia data is validated for lung cancer and GISTs
•Effective C_ave can predict success of clinical repurposing efforts from in vitro data

Summary

Drug repositioning seeks to leverage existing clinical knowledge to identify alternative clinical settings for approved drugs. However, repositioning efforts fail to demonstrate improved success rates in late-stage clinical trials. Focusing on 11 approved kinase inhibitors that have been evaluated in 139 repositioning hypotheses, we use data mining to characterize the state of clinical repurposing. Then, using a simple experimental correction with human serum proteins in in vitro pharmacodynamic assays, we develop a measurement of a drug’s effective exposure. We show that this metric is remarkably predictive of clinical activity for a panel of five kinase inhibitors across 23 drug variant targets in leukemia. We then validate our model’s performance in six other kinase inhibitors for two types of solid tumors: non-small cell lung cancer (NSCLC) and gastrointestinal stromal tumors (GISTs). Our approach presents a straightforward strategy to use existing clinical information and experimental systems to decrease the clinical failure rate in drug repurposing studies.