Predicting Solubility of Newly-Approved Drugs (2016–2020) with a Simple ABSOLV and GSE(Flexible-Acceptor) Consensus Model Outperforming Random Forest Regression

Abstract

This study applies the ‘Flexible-Acceptor’ variant of the General Solubility Equation, GSE( Φ,B ), to the prediction of the aqueous intrinsic solubility, log 10 S 0 , of FDA recently-approved (2016–2020) ‘small-molecule’ new molecular entities (NMEs). The novel equation had been shown to predict the solubility of drugs beyond Lipinski’s ‘Rule of 5’ chemical space (bRo5) to a precision nearly matching that of the Random Forest Regression (RFR) machine learning method. Since then, it was found that the GSE( Φ,B ) appears to work well not only for bRo5 NMEs, but also for Ro5 drugs. To put context to GSE( Φ,B ), Yalkowsky’s GSE(classic), Abraham’s ABSOLV, and Breiman’s RFR models were also applied to predict log 10 S 0 of 72 newly-approve NMEs, for which useable reported solubility values could be accessed (nearly 60% from FDA New Drug Application published reports). Except for GSE (classic), the prediction models were retrained with an enlarged version of the Wiki- pS 0 database (nearly 400 added log 10 S 0 entries since our recent previous study). Thus, these four models were further validated by the additional independent solubility measurements which the newly-approved drugs introduced. The prediction methods ranked RFR ~ GSE ( Φ,B ) > ABSOLV > GSE (classic) in performance. It was further demonstrated that the biases generated in the four separate models could be nearly eliminated in a consensus model based on the average of just two of the methods: GSE ( Φ,B ) and ABSOLV. The resulting consensus prediction equation is simple in form and can be easily incorporated into spreadsheet calculations. Even more significant, it slightly outperformed the RFR method.

Trends in PhysChem Properties of Newly Approved Drugs over the Last Six Years; Predicting Solubility of Drugs Approved in 2021

“…The method is particularly appealing since it requires no 'training.' Merely the melting point (mp in o C) and the octanolwater partition coe cient, either measured (log P) or calculated (clogP), are prerequisites for predicting solubility (log molar units): The thermodynamic basis of the equation was reviewed recently [13]. Brie y, the dissolution of a crystalline substance in water consists of two main contributions: (i) crystal lattice effect (XTL), i.e., the energy needed to break down the lattice to form a hypothetical 'supercooled liquid' (SCL), and (ii) solvation effect, i.e., the energy released as the SCL dissolves in water.…”

“…The c-coe cients as functions of Φ + B were determined by partial least squares (PLS open-source package from https://cran.r-project.org/web/packages/pls) analysis of solubility data sorted on values of Φ + B and uniformly binned into 18 groups of 123-775 points, to ensure nearly constant Φ + B increments, as described previously [12,13]. Since our last study [13], the database has accumulated nearly 1000 new entries. So, a new set of bconstants was determined in the current investigation, using drug-relevant molecules as the training set, but excluding new drugs from the training.…”

“…The Wiki-pS 0 intrinsic aqueous solubility database of mostly druglike molecules (currently with 7655 deeply-curated entries) was used to train the ABSOLV, GSE(Φ,B) and RFR models. Several hundred values from the database have already been published [10][11][12][13][63][64][65][66][67][68][69][70][71][72][73][74], and the entire database is currently being prepared for publication as a book. The newly-approved drugs were used as external test sets and were excluded from the training process.…”

“…A comprehensive mass-action nonlinear regression program, pDISOL-X, to analyze solubility-pH data was developed in parallel, with enhancements added periodically. To predict drug solubility, different computational methods were examined in a series of studies [10][11][12][13]. Initially [10], Breiman's Random Forest regression (RFR) machine learning method [14] was tested and compared to predictions of Yalkowsky's General Solubility Equation (GSE) [15] and Abraham's Solvation Equation (ABSOLV) [16].…”

“…A way to modify the traditional GSE by implementing Kier's molecular exibility index (Φ) and Abraham's basicity descriptor (B) resulted in the novel 'FlexibleAcceptor' GSE(Φ,B) equation [12]. This equation was directed [13] to predict the intrinsic solubility values of 72 NMEs recently-approved by the FDA (20162020) [17][18][19][20][21].…”