Predicting Solubility of New Drugs: Handbook of Critically Curated Data for Pharmaceutical Research

by Alex Avdeef

Overview

In pharmaceutical research, solubility plays a key part in the assessment of pharmacokinetic risks. Poor drug absorption, reduced efficacy, excessive metabolism and adverse reactions are frequently related to issues of drug solubility. During early discovery research at pharmaceutical companies, many thousands of molecules are considered. Most are rejected due to perceived unfavorable properties. Here the author uses the so-called Wiki-pS0TM database which forms the backbone of this unique handbook. Also discussed is the emerging class of therapeutically-promising research molecules called PROTACs (proteolysis-targeting chimeras), showing a propensity for ‘undruggable’ targets.

FEATURES

• Comprehensive and unique listing of measured aqueous intrinsic solubility focusing on drug-like and drug-relevant molecules.
• The database can be used to predict the solubility of research pharmaceutical molecules.
• Includes downloadable files of the database (csv format).
• The mining of the database can result in a better design of solubility assay protocols, leading to better quality of measurements.
  
• Artificial intelligence and Bayesian statistics will likely be key to this subject area in the future.

Product Details

ISBN-13:	9781032617671
Publisher:	CRC Press
Publication date:	05/27/2024
Pages:	1730
Product dimensions:	7.00(w) x 10.00(h) x (d)

About the Author

Dr. Alex Avdeef is an AAPS Fellow since 2014, formerly, a Visiting Senior Research Fellow at King’s College London, and the author of Absorption and Drug Development, 2nd Ed., Wiley, 2012. In 2021, the book was translated into Chinese, with translators affiliated with the China FDA. For nearly 50 years, he has been teaching, researching, and developing methods, instruments, and analysis software for measurement of ionization constants, solubility, dissolution, and permeability of drugs. His accomplishments in the development of instrumentation include several well-known instruments which are or recently have been manufactured by leading companies in the instrument market, including Thermo Fisher Scientific, Sirius Analytical and Pion Inc. He has over 200 technical publications in primary scientific journals and in book chapters. He has written several comprehensive technical guides, and co-invented in 6 patents. He co-founded Sirius Analytical (UK) in 1989, pION INC (USA) in 1996, and founded in-ADME Research (NYC) in 2011. His other positions were at Orion Research (USA), Syracuse University (USA), UC Berkeley (USA), and Caltech (Pasadena, USA).

Table of Contents

1 Introduction

1.1 ‘Not too little – not too much’,

1.2 Why a Database of Aqueous Intrinsic Solubility?

1.3 Database

1.4 Measurements Can Be Improved

1.5 Solubility-pH Profiles, Intrinsic Solubility, and Profile Distortions

2 Physicochemical Properties of Wiki-pS₀ Database Molecules

2.1 Most Molecules in Database are Drug-Like or Drug-Relevant

2.2 Distribution of Intrinsic Solubility

2.3 Interlaboratory Variance

2.4 Quality and Chemical Space of Experimental Data

2.5 PROTACs: Lipinski’s ‘Rule Of 5’ Characteristics

2.6 Newly-Approved Drugs: Lipinski’s ‘Rule Of 5’ Characteristics

2.7 Kier Flexibility Index, Φ, and Abraham H-Bond Acceptor Potential, B

2.8 Principal Component Analysis

2.9 Quantitative Estimate of Drug-Likeness

3 Solubility Prediction Methods

3.1 Overview of Solubility Prediction Models

3.2 Gap between Prediction and Measurement

3.3 Yalkowsky General Solubility Equation (GSE)

3.4 ‘Flexible-Acceptor’ General Solubility Equation, GSE(Φ,B)

3.5 Abraham Solvation Equation (ABSOLV)

3.6 Breiman Random Forest Regression

4 Predicting of Solubility of PROTACs

4.1 Determination of the Three GSE(Φ,B) Coefficients from Training Set Iso-(Φ+B) Bins

4.2 ‘Flexible-Acceptor’ Lipophilicity

4.3 ABSOLV Trained to Predict the Intrinsic Solubility of PROTACs

4.4 RFR Training

4.5 Training Set Performances

4.6 Effect of Small Amounts of DMSO (≤ 5 vol%)

4.7 Predicting Solubility of PROTACs

5 Predicting of Solubility of New Drugs

5.1 Trends in Physicochemical Properties of Emerging Drugs

5.2 Characteristics of Emerging Drugs (2016-2022)

5.3 Re-training of the Training Sets

5.4 Predicting Solubility of Newly-Approved Drugs

5.5 Striving for Similarity Between Training Set and Test Set

6 Instruments with ‘Intelligence’

6.1 Bjerrum Difference Plots for Saturated Solutions - Normalized Titration Curves

6.2 ‘Intelligent’ Assay: Noyes-Whitney ‘Dissolution Titration Template’ (DTT) Method

6.3 High-Throughput Solubility Instrument with DMSO Bias Correction

6.4 Where to Aim Next

Appendix - Data Sources, Solubility Definitions, Unit Conversions

A1 Data Sources in Wiki-pS₀ Database

A1.1 ‘Kinetic Solubility’ Measurements

A1.2 Data for FDA Newly-Approved Drugs (2016-2022)

A1.3 Data from Secondary Sources

A.1.4 Single-Source Measurements

A1.5 Data from Miscellaneous Primary Sources

A1.6 Sources of pK_a Data

A2 Definitions, Supersaturation, Cosolvents

A2.1 Consensus Recommendations

A2.2 pH Measurement

A3 Solubility Units – Conversions to Molarity

A4 Different Types of Aqueous Solubility of Ionizable Molecules

A4.1 Single-Point Water Solubility of Free Acid/Base (S_α /S_β for Free Acid/Base, or Simply S_w)

A4.2 Single-Point Solubility at a Particular Buffered pH (S_pH)

A4.3 Single-Point Intrinsic Solubility (S₀)

A4.4 Single-Point Water Solubility of Non-Disproportionating μ-Type Salt (S_salt or S_μ)

A4.5 Single-Point Water Solubility of Disproportionating δ-Type Salt (S_δ )

General References

Tabulation Organization and Notes

TABULATION 1 - Wiki-pS₀

TABULATION 2 - DMSO Bias-Corrected Solubility

Tabulation References

Index of Topics

Index of Molecule Names

Index of Registry Numbers (RN)