Koichi Handa

Artificial intelligence in drug discovery — what it is, where we stand and the path forward

Andreas Bender, Morgan C. Thomas, Jack W. Scannell, David A. Shaywitz et autres

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Scaling Exponents for Human CL and Vdss

Koichi Handa, Shogo Matsumoto, Hiroki Takano

15 Reliable pharmacokinetic (PK) translation into humans is a cornerstone of first-in-human (FIH) 16 dose selection, providing the exposure estimates needed to design efficient and informative 17

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Advances in In silico predictive models for DDI prediction: Implications and practical applications in drug discovery

Koichi Handa, Hideaki Mamada, Shinji Nakayama, Tadahaya Mizuno et autres

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Investigation of Allometric Scaling Exponents for mAb PK Prediction and Evaluation of Machine Learning as an Alternative Approach

Koichi Handa, Sasagu Kawano, Hiroki Takano

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Fraction-based Linear Extrapolation (FLEX) Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach

Yuki Umemori, Koichi Handa, Saki Yoshimura, Michiharu Kageyama

Computational approaches to DMPK: A realistic assessment of current methods and their practical impact. Part I: Physicochemical and in vitro properties

Koichi Handa, Mariko Hirano, Michiharu Kageyama, Andreas Bender

Artificial intelligence and computational approaches have received considerable interest in recent years, and here we assess their real-world utility in drug discovery projects. We review recent in silico models in the area of drug metabolism and pharmacokinetics (DMPK), especially for physicochemical properties …

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Fraction-based Linear Extrapolation (FLEX) Method for Predicting Human Pharmacokinetic Clearance: Advanced Allometric Scaling Method and Machine Learning Approach

Yuki Umemori, Koichi Handa, Saki Yoshimura, Michiharu Kageyama

Accurate prediction of human pharmacokinetic (PK) parameters, particularly clearance (CL), is critical in early-stage drug development. Single Species Scaling (SSS) using rat PK data, with or without unbound plasma fraction (fu,plasma), is commonly used; however, its predictive accuracy is often compromised for …

Machine Learning Prediction and Validation of Plasma Concentration–Time Profiles

Hiroaki Iwata, Michiharu Kageyama, Koichi Handa

Recent research has increasingly focused on using machine learning for covariate selection in population pharmacokinetics (PPK) analysis. However, few studies have explored the prediction of plasma concentration profiles of drugs using nonlinear mixed-effect models combined with machine learning. This gap includes limited …

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Predicting Plasma Concentration–Time Profiles of Complex Drugs with Double Peaks Using Machine Learning: A Case Study with Veralipride

Koichi Handa, Yuki Umemori, Michiharu Kageyama, Hiroaki Iwata

Research utilizing artificial intelligence for population pharmacokinetic (PPK) analysis has advanced recently. We previously validated the utility of machine learning (ML) models to predict the plasma concentration (Cp)–time profiles of remifentanil. However, Cp prediction of complex drugs is essential for drug safety …

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A Practical In Silico Method for Predicting Compound Brain Concentration–Time Profiles: Combination of PK Modeling and Machine Learning

Koichi Handa, Daichi Fujita, Mariko Hirano, Saki Yoshimura et autres

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood–brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for …

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A practical in silico method for predicting compound brain concentration-time profiles: combination of PK modeling and machine learning

Koichi Handa, Daichi Fujita, Mariko Hirano, Saki Yoshimura et autres

Given the aging populations in advanced countries globally, many pharmaceutical companies have focused on developing central nervous system (CNS) drugs. However, due to the blood-brain barrier, drugs do not easily reach the target area in the brain. Although conventional screening methods for …

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Prediction of Inhibitory Activity against the MATE1 Transporter via Combined Fingerprint- and Physics-Based Machine Learning Models

Koichi Handa, Shunta Sasaki, Satoshi Asano, Michiharu Kageyama et autres

High Resolution Image Download MS PowerPoint Slide Renal secretion plays an important role in excretion of drug from the kidney. Two major transporters known to be highly involved in renal secretion are MATE1/2 K and OCT2, the former of which is highly …

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