As known, the term “solubility” in chemistry is defined as the concentration of the saturated solution of a target solute in a specified solvent at a given temperature. It affects various physicochemical characteristics of compounds, such as adsorption and bioavailability, hence solubility data is critical for the successful flow of drug development studies [1-4].
At the same time, solubility measurements face a number of challenges: they are resource-demanding and time-consuming, and thus are often skipped in the screening programs or shifted to later stages of drug discovery algorithms. This, in turn, may bring about failures during compound testing due to unexpected low solubility and hindered activity of a drug candidate. In order to save time and resources and resolve the solubility data issues, various approaches have been proposed, including different experimental procedures, computational methods, and data-sharing programs.
In general, experimental solubility measurement techniques can be broadly divided into “excess solvent” and “excess solid” methods . Within the former method, the solvent is gradually added to the solute, and/or the temperature is increased until the equilibrium saturated state is achieved (Fig. 1a), while the latter approach relies on adding solute to the solvent until the undissolved fraction is observed (Fig. 1b).
Figure 1. Experimental solubility measurement techniques: “excess solvent” and “excess solid” methods
Both methods have advantages and limitations. For the “excess solvent” techniques, visual detection of the result may be easily automated [1, 6], making it fast and applicable at small scales; however, temperature measurements lack accuracy in this case, and chemical composition of the solute cannot be checked. On the other hand, slower and more resource-consuming “excess solid” methods are beneficial wherever the physicochemical properties of the solid have to be identified, and – in combination with filtration – provide the highest quality results. These measurements are performed using a variety of techniques, such as classical titration, gravimetric methods, UV-spectrometry, NMR, or high-performance liquid chromatography (HPLC), used in the majority of solubility screening platforms.
Solvent is an important factor for solubility studies. Depending on particular protocol assays or storage conditions, water, saline, ethanol, dimethyl sulfoxide (DMSO), phosphate-buffered saline (PBS), etc., can be used to dissolve a target compound. For instance, PBS is a buffer solution which allows maintaining a constant pH and is isotonic to human blood. DMSO is a non-toxic solvent, able to dissolve both polar and non-polar substances and preserve stock solutions of test compounds in extensive chemical libraries . Water, or aqueous, solubility, which directly correlates with octanol/water partition coefficient , is one of the crucial biopharmaceutical properties defining the transport, release, and absorption of a drug substance in the body.
Due to its fundamental importance, in addition to experimental measuring, aqueous solubility is seeked to be predicted analytically [9, 10] and numerically  via the statistical and thermodynamic approaches. The former strategy comprises such computational methods as quantitative structure–property relationships (QSPR) and data mining, which provide reliable predictions yet in some cases lack physical relevance. On the other hand, thermodynamics-based methods, involving the calculation of the solvation free energy and solution of corresponding equations, are much more difficult to implement. Proceeding from the above, different models must be combined to enhance the accuracy of aqueous solubility estimations .
At Life Chemicals, we successfully use both thermodynamic and kinetic HTS solubility measurement methods. This service is available on request together with an array of complementary in vitro ADMET tests and customizable quality assurance services.
Moreover, we offer an off-the-shelf collection of soluble fragment-like molecules (Fig. 2):
- Fragment Library with Experimental Solubility: 22,500 stock available fragments with experimentally confirmed solubility in DMSO and PBS
- High Solubility Fragment Subset: 6,500 fragments with minimum experimentally confirmed solubility in PBS at 1 mM and in DMSO at 200mM, measured by the thermodynamic method using HPLC
- Diversity Screening Subsets of Soluble Fragments: screening pools of 1,280, 960 and 320 drug-like low molecular weight fragments, also available in the pre-plated format
- Pre-plated Soluble Fluorine-containing Fragment Set: 1,350 fluorine-containing fragments with experimentally assured solubility at 200 mM in the DMSO solution
- Fluorine Fragment Cocktails: 130 sets of 10 in-stock drug-like fluorine-containing fragments each (total 1,300 screening compounds) with the most different 19F chemical shifts in order to facilitate screening results interpretation
Please, contact us at email@example.com for any additional information and price quotations.
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Custom compound selection based on specific parameters can be performed on request, with competitive pricing and the most convenient terms provided.
Figure 2. Representative compounds from the Life Chemicals Fragment Library with Experimental Solubility.
- T. Sou, C. A.S. Bergström (2018). Automated assays for thermodynamic (equilibrium) solubility determination. Drug Discovery Today: Technologies, 27, 11-19. DOI: 10.1016/j.ddtec.2018.04.004
- Savjani, K. T., Gajjar, A. K., & Savjani, J. K. (2012). Drug solubility: importance and enhancement techniques. ISRN pharmaceutics, 2012, 195727.
- DOI: 10.5402/2012/195727
- Li Di, Paul V. Fish, Takashi Mano. (2012) Bridging solubility between drug discovery and development, Drug Discovery Today, 17, 9–10, 2012, 486-495, DOI: 10.1016/j.drudis.2011.11.007
- Coltescu A. R., Butnariu M, Sarac I. (2020). The Importance of Solubility for New Drug Molecules. Biomed Pharmacol J;13(2). DOI: 10.13005/bpj/1920
- S. Black, L. Dang, C. Liu, and H. Wei. (2013). On the Measurement of Solubility. Organic Process Research & Development 17 (3), 486-492. DOI: 10.1021/op300336n
- P. Shiri, V. Lai, T. Zepel, et al. (2021). Automated solubility screening platform using computer vision, iScience, 24, 3, 102176, DOI: 10.1016/j.isci.2021.102176
- Balakin K. V., Ivanenkov Y. A., Skorenko A. V., Nikolsky Y. V., Savchuk N. P., Ivashchenko A. A. (2004). In Silico Estimation of DMSO Solubility of Organic Compounds for Bioscreening. European Journal of International Relations. 9(1):379-404. DOI:10.1177/1354066108092304
- C. Hansch, J. E. Quinlan, and G. L. Lawrence. (1968). The linear free energy relationship between partition coefficients and the aqueous solubility of organic liquids. J. Org. Chem. 33:347–350.
- Gao H, Shanmugasundaram V, Lee P. (2002). Estimation of aqueous solubility of organic compounds with QSPR approach. Pharm Res. 19(4):497-503. DOI: 10.1023/a:1015103914543
- Y. Ran, N. Jain, and S. H. Yalkowsky. (2001). Prediction of Aqueous Solubility of Organic Compounds by the General Solubility Equation (GSE). Journal of Chemical Information and Computer Sciences 41 (5), 1208-1217. DOI: 10.1021/ci010287z
- Palmer, D. S.; McDonagh, J. L.; Mitchell, J. B. O.; van Mourik, T.; Fedorov, M. V. (2012). First-Principles Calculation of the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules. Journal of Chemical Theory and Computation. 8 (9): 3322–3337. DOI: 10.1021/ct300345m
- McDonagh, J. L.; Nath, N.; De Ferrari, L.; van Mourik, T.; Mitchell, J. B. O. (2014). Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules. Journal of Chemical Information and Modeling. 54 (3): 844–856. DOI: 10.1021/ci4005805