Multi-lab intrinsic solubility measurement reproducibility in CheqSol and shake-flask methods

This commentary compares 233 CheqSol intrinsic solubility values (log S0) reported in the Wiki-pS0 database for 145 different druglike molecules to the 838 log S0 values determined mostly by the saturation shake-flask (SSF) method for 124 of the molecules from the CheqSol set. The range of log S0 spans from -1.0 to -10.6 (log molar units), averaging at -3.8. The correlation plot between the two methods indicates r2 = 0.96, RMSE = 0.34 log unit, and a slight bias of -0.07 log unit. The average interlaboratory standard deviation (SDi) is slightly better for the CheqSol set than that of the SSF set: SDiCS = 0.15 and SDiSSF = 0.24. The intralaboratory errors reported in the CheqSol method (0.05 log) need to be multiplied by a factor of 3 to match the expected interlaboratory errors for the method. The scale factor, in part, relates to the hidden systematic errors in the single-lab values. It is expected that improved standardizations in the ‘gold standard’ SSF method, as suggested in the recent ‘white paper’ on solubility measurement methodology, should make the SDi of both methods be about ~0.15 log unit. The multi-lab averaged log S0 (and the corresponding SDi) values could be helpful additions to existing training-set molecules used to predict the intrinsic solubility of drugs and druglike molecules

Računarski modeli za predviđanje rastvorljivosti lekova

Aqueous solubility of a drug is a factor which can significantly influence its oral bioavailability, and can also affect the drug distribution in the body. Consideration of aqueous solubility in early stages of drug discovery and development is vital in reducing the incidence of late-stage drug development failures. The application of computational models for solubility prediction could provide the screening of combinatorial libraries, helping single-out potentially problematic and eliminate compounds with inadequate solubility. In addition to the prediction of solubility from chemical structure, the interpretation of such models can give an insight into structure-solubility relationships and can guide the optimization of structures in order to provide better solubility whilst retaining the activity of the investigated drugs. Development of such models is a complex process that requires consideration of numerous factors which can impact the final model's performance. Different solubility modeling approaches are discussed in this article. Despite intensive research on model development, prediction of the solubility of diverse drugs remains a challenging task. The quality of available experimental data used for modeling of solubility is increasingly recognized as one of the main causes for the limited reliability of many of the proposed models. Therefore, the full potential of the developed modeling methods will only be achieved by greater availability of reliable data obtained by same experimental methodology.Rastvorljivost leka u vodi je faktor koji može značajno da utiče na bioraspoloživost peroralno primenjenog leka, kao i na njegovu raspodelu u organizmu. Razmatranjem rastvorljivosti u ranim fazama otkrića i razvoja leka smanjuje se mogućnost neuspeha u daljem razvoju leka. Računarske metode za predviđanje rastvorljivosti lekova omogućavaju analizu kombinatornih baza podataka, identifikaciju potencijalno problematičnih jedinjenja i isključivanje onih čija je rastvorljivost neadekvatna. Pored predviđanja rastvorljivosti na osnovu hemijske strukture, analizom ovih modela moguće je detaljnije razjasniti odnose hemijske strukture i rastvorljivosti ispitivanih lekova i optimizovati strukture u cilju poboljšanja rastvorljivosti, pri čemu bi njihova aktivnost ostala nepromenjena. Razvoj ovakvih modela je kompleksan proces koji zahteva razmatranje velikog broja faktora koji mogu uticati na uspešnost predviđanja konačnog modela. U ovom radu su prikazani različiti pristupi koji se koriste u razvoju računarskih modela za predviđanje rastvorljivosti. I pored intenzivnog rada na razvoju ovih modela tokom protekle decenije, pouzdanost predviđanja rastvorljivosti lekova različitih struktura još uvek ostaje veliki izazov. Kvalitet dostupnih eksperimentalnih podataka koji se koriste u modelovanju rastvorljivosti se u sve većoj meri prepoznaje kao jedan od glavnih uzroka ograničene pouzdanosti većine do sada predloženih modela. Iskorišćenje punog potencijala razvijenih pristupa modelovanja rastvorljivosti uslovljeno je širom dostupnošću pouzdanih podataka za rastvorljivost određenih pod identičnim eksperimentalnim uslovima

Global testing of a consensus solubility assessment to enhance robustness of the WHO biopharmaceutical classification system

The WHO Biopharmaceutical Classification System (BCS) is a practical tool to identify active pharmaceutical ingredients (APIs) that scientifically qualify for a waiver of in vivo bioequivalence studies. The focus of this study was to engage a global network of laboratories to experimentally quantify the pH-dependent solubility of the highest therapeutic dose of 16 APIs using a harmonized protocol. Intra-laboratory variability was ≤5 %, and no apparent association of inter-laboratory variability with API solubility was discovered. Final classification “low solubility” vs “high solubility” was consistent among laboratories. In comparison to the literature-based provisional 2006 WHO BCS classification, three compounds were re-classified from “high” to “low-solubility”. To estimate the consequences of these experimental solubility results on BCS classification, dose-adjusted in silico predictions of the fraction absorbed in humans were performed using GastroPlus®. Further expansion of these experimental efforts to qualified APIs from the WHO Essential Medicines List is anticipated to empower regulatory authorities across the globe to issue scientifically-supported guidance regarding the necessity of performing in vivo bioequivalence studies. Ultimately, this will improve access to affordable generic products, which is a critical prerequisite to reach Universal Health Coverage

Structure-based Generalized Models for Pure-fluid Saturation Properties and Activity Coefficients

Structure-based generalized models were developed for a priori predictions of pure-fluid saturation properties, and for vapor-liquid equilibrium (VLE) of binary mixtures. Specifically, Quantitative Structure-Property Relationships (QSPR) modeling was used to provide structure-based parameters for (a) the Scaled-Variable-Reduced-Coordinate (SVRC) saturation property model, and (b) the Non-Random Two-Liquid (NRTL) and the Universal Quasi-Chemical (UNIQUAC) activity coefficient models. A representative database comprised of diverse molecular species was utilized for these generalizations. The SVRC-QSPR model generalizations for vapor pressure and saturated phase densities yielded predictions with absolute average deviation (AAD) of 1%. Similarly, the NRTL-QSPR and UNIQUAC-QSPR activity coefficient models produced VLE predictions within twice the AAD of the data regressions. The results of this preliminary study demonstrate the efficacy of using theory-framed QSPR modeling for generalizing saturation property and phase equilibrium models.School of Chemical Engineerin

Prediction of aqueous intrinsic solubility of druglike molecules using Random Forest regression trained with Wiki-pS0 database

The accurate prediction of solubility of drugs is still problematic. It was thought for a long time that shortfalls had been due the lack of high-quality solubility data from the chemical space of drugs. This study considers the quality of solubility data, particularly of ionizable drugs. A database is described, comprising 6355 entries of intrinsic solubility for 3014 different molecules, drawing on 1325 citations. In an earlier publication, many factors affecting the quality of the measurement had been discussed, and suggestions were offered to improve ways of extracting more reliable information from legacy data. Many of the suggestions have been implemented in this study. By correcting solubility for ionization (i.e., deriving intrinsic solubility, S0) and by normalizing temperature (by transforming measurements performed in the range 10-50 °C to 25 °C), it can now be estimated that the average interlaboratory reproducibility is 0.17 log unit. Empirical methods to predict solubility at best have hovered around the root mean square error (RMSE) of 0.6 log unit. Three prediction methods are compared here: (a) Yalkowsky’s general solubility equation (GSE), (b) Abraham solvation equation (ABSOLV), and (c) Random Forest regression (RFR) statistical machine learning. The latter two methods were trained using the new database. The RFR method outperforms the other two models, as anticipated. However, the ability to predict the solubility of drugs to the level of the quality of data is still out of reach. The data quality is not the limiting factor in prediction. The statistical machine learning methodologies are probably up to the task. Possibly what’s missing are solubility data from a few sparsely-covered chemical space of drugs (particularly of research compounds). Also, new descriptors which can better differentiate the factors affecting solubility between molecules could be critical for narrowing the gap between the accuracy of the prediction models and that of the experimental data

Toward accurate high-throughput physicochemical profiling using image-based single-particle analysis

Key physicochemical properties determining the developability of a drug include solubility, dissolution rate, lipophilicity and pKa. Not only do these properties affect synthesis and solid form optimization, choice of administration route, processability and formulation strategies; they also greatly influence, directly or indirectly, the absorption, distribution, metabolism, excretion, toxicity and efficacy of drugs. However, miniaturized methods that would enable small-scale determination of these fundamental properties in an accurate and rapid way, are lacking. Image-based microscopy could provide an opportune method for non-specific, rapid and miniaturized applications. First, the applicability of image-based microscopy and single-particle analysis in drug dissolution rate measurement was evaluated. This was done by comparing image analysis data with traditional UV spectrophotometric data of individual dissolving drug pellets. It was found that dissolution rates obtained by image analysis and UV spectrophotometry were practically identical. Next, a single-particle trap flow-through device was developed, wherein it is possible to continuously monitor individual drug particles under constant flow conditions. Based on the promising results of image-based dissolution rate analysis, the possibility of acquiring the intrinsic dissolution rate from individual freely rotating particles, trapped inside the flow through device, was evaluated. It was found that image analysis can be used for rapid real-time determination of intrinsic dissolution rates from continuously changing effective surface areas of dissolving individual micro-particles. The method was then further extended to determine the equilibrium solubility of drugs. Based on the diffusion layer dissolution rate model, solubility is the rate limiting factor of dissolution and can therefore be determined. While solubility is generally determined from bulk solutions after long incubation times, it was shown that the equilibrium solubility can be rapidly determined from individual pure-substance particles by means of the diffusion layer theory and image analysis. Finally, the single-particle method was further miniaturized and a second device developed, in order to allow imaging of individual powder crystals. It was shown that dissolution rate and solubility can be acquired from individual nanogram crystals. The single-particle method was further extended to acquire pKa, logP and logD of the studied substances, using aqueous buffers, simulated physiological solutions and organic solvents. Using this method and device, it is possible to acquire a complete pH-solubility profile for an unknown material of unknown composition, with individual measurements of less than 30 seconds. In summary, these results strongly suggest that image-based analysis of materials could be applied in high-throughput experimentation (HTE) applications. The possibility of acquiring solubility, dissolution rate, lipophilicity and pKa using a single analytical method, could significantly simplify and speed up accurate data acquisition. This in turn, could lead to faster and more informed decision-making and, ultimately, better and more affordable drugs.Tärkeimpiä uusien lääkeaineiden kehityskelpoisuutta kuvaavia fysikaaliskemiallisia ominaisuuksia ovat aineen liukoisuus, liukenemisnopeus, lipofiilisyys sekä pKa. Nämä ominaisuudet vaikuttavat lääkekehityksessä uuden aineen synteesin ja kiinteän muodon optimointiin, annostelureitin valintaan, aineen käsiteltävyyteen sekä formulaatiostrategian valintaan. Laajemmin nämä ominaisuudet vaikuttavat joko suorasti tai epäsuorasti lääkeaineen imeytymiseen, jakautumiseen, metaboliaan, eliminaatioon, toksisuuteen ja tehoon. Miniaturisoidut menetelmät, jotka mahdollistavat näiden kriittisten ominaisuuksien määrittämisen tarkasti ja nopeasti pienessä mittaskaalassa, ovat kuitenkin harvinaisia. Kuvantava mikroskopia voisi tarjota otollisen menetelmän epäspesifiseen, nopeaan ja miniaturisoituun analyysiin. Työn ensimmäisessä osassa tutkittiin kuvantavan mikroskopian ja yksittäisten hiukkasten analyysin soveltuvuutta lääkeaineiden liukenemiskokeissa. Kuva-analyysin kautta määritettyjen yksittäisten liukenevien hiukkasten liukenemisnopeuskäyriä verrattiin perinteisen UV-spektrofotometrisen detektion kautta, samoista hiukkasista määritettyihin, liukenemiskäyriin. Tulokset osoittivat, että kuva-analyysin ja UV spektrofotometrian avulla määritetyt liukenemisnopeuskäyrät ovat käytännössä yhdenvertaisia. Työn ensimmäisen vaiheen lupaaviin tuloksiin perustuen, työn toisessa vaiheessa tutkittiin kuva-analyysiin perustuvan menetelmän käyttöä aineen ominaisliukenemisnopeuden määrittämisessä. Tämän mahdollistamiseksi kehitettiin hiukkasloukku-läpivirtauskammio, joka mahdollistaa yksittäisten paikallaan pyörivien hiukkasten kuvaamisen jatkuvassa nestevirtauksessa. Tulokset osoittivat että kuva-analyysiin perustuva aineen ominaisliukenemisen nopea mittaaminen, yksittäisistä jatkuvasti pinta-alaltaan muuttuvista mikrohiukkasista, on mahdollista. Tämän jälkeen menetelmää kehitettiin edelleen aineen tasapainoliukoisuuden määrittämiseen. Diffuusiokerrokseen perustuvan liukenemisnopeusmallin mukaisesti, aineen liukoisuudesta tulee liukenemisnopeutta rajoittava tekijä ja liukoisuus on näin ollen määritettävissä liukenemisnopeudesta. Yleensä aineen tasapainoliukoisuuden määrittäminen tapahtuu nestefaasista pitkien inkubaatioaikojen jälkeen. Kolmannen osatyön tulokset osoittivat kuitenkin, että aineen tasapainoliukoisuus voidaan nopeasti määrittää myös yksittäisistä puhdasainehiukkasista kuva-analyysin kautta, diffuusiokerrosmalliin perustuen. Viimeisessä osatyössä menetelmää miniaturisoitiin edelleen ja kehitettiin uusi laite, joka mahdollistaa aineen liukoisuusparametrien määrittämisen alle 30 sekunnissa, yksittäistä jauhehiukkasista. Työssä osoitettiin että yksittäisistä muutamia nanogrammoja painavista jauhehiukkasista on mahdollista kuva-analyysin kautta määrittää aineen liukenemisnopeus sekä liukoisuus. Menetelmää laajennettiin myös aineen pKa, logP sekä logD arvojen määrittämiseen. Tämän mahdollistamiseksi käytettiin vesipohjaisia puskureita, maha-suolikanavan nesteitä simuloivia liuoksia sekä orgaanisia liuottimia. Kehitettyä menetelmää ja laitetta käyttämällä on mahdollista määrittää pH-liukoisuusprofiili tuntemattomalle puhdasaineelle. Yhteenvetona, tässä väitöstyössä saavutetut tulokset osoittavat vahvasti, että kuva-analyysiin perustuva aineiden analyysiä voidaan soveltaa nopean seulonnan kokeissa. Mahdollisuus määrittää aineen liukoisuus, liukenemisnopeus, lipofiilisyys sekä pKa, käyttämällä yhtä ainoata määrittämismenetelmää, voi merkittävästi yksinkertaistaa ja nopeuttaa tarkan mittaustiedon saamista. Tämä vuorostaan johtaisi lääkekehityksessä nopeutettuun ja varmempaan päätöksentekoon sekä viime kädessä parempiin ja edullisimpiin lääkevalmisteisiin

Aqueous solubility of drug-like compounds

New effective experimental techniques in medicinal chemistry and pharmacology have resulted in a vast increase in the number of pharmacologically interesting compounds. However, the possibility of producing drug candidates with optimal biopharmaceutical and pharmacokinetic properties is still improvable. A large fraction of typical drug candidates is poorly soluble in water, which results in low drug concentrations in gastrointestinal fluids and related acceptable low drug absorption. Therefore, gaining knowledge to improve the solubility of compounds is an indispensable requirement for developing compounds with drug-like properties. The main objective of this thesis was to investigate whether computer-based models derived from calculated molecular descriptors and structural fragments can be used to predict aqueous solubility for drug-like compounds with similar structures. For this purpose, both experimental and computational studies were performed. In the experimental work, a novel crystallization method for weak acids and bases was developed and applied for European patent. The obtained crystalline materials could be used for solubility measurements. A novel recognition method was developed to evaluate the tendency of compounds to form amorphous forms. This method could be used to ensure that only solubilities of crystalline materials were collected for the development of solubility prediction. In the development of improved in silico solubility models, lipophilicity was confirmed as the major driving factor and crystal information related descriptors as the second important factor for solubility. Reasons for the limited precision of commercial solubility prediction tools were identified. A general solubility model of high accuracy was obtained for drug-like compounds in congeneric series when lipophilicity was used as descriptor in combination with the structural fragments. Rules were derived from the prediction models of solubility which could be used by chemists or interested scientists as a rough guideline on the contribution of structural fragments on solubility: Aliphatic and polar fragments with high dipole moments are always considered as solubility enhancing. Strong acids and bases usually have lower intrinsic solubility than neutral ones. In summary, an improved solubility prediction method for congeneric series was developed using high quality solubility results of drugs and drug precursors as input parameter. The derived model tried to overcome difficulties of commercially available prediction tools for solubility by focusing on structurally related series and showed higher predictive power for drug-like compounds in comparison to commercially available tools. Parts of the results of this work were protected by a patent application1, which was filed by F. Hoffmann-La Roche Ltd on August 30, 2005