Thermodynamics and Relative Solubility Prediction of Polymorphic Systems

Abramov, Yuriy A.; Pencheva, Klimentina

doi:10.1002/9781119600800.ch22

Cited by 7 publications

(9 citation statements)

References 44 publications

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“…Essentially, for a system supersaturated with respect to both polymorphs, the model identifies that low supersaturation with respect to the stable polymorph (S st ) leads to an extremely small supersaturation with respect to the metastable polymorph (S me ), radically driving up the critical free energy with respect to the metastable polymorph. Generally, high supersaturations sometimes much higher than the upper limit of the metastable zone, are required to kinetically favour the metastable polymorph.This work follows the report of Abramov that, with some exceptions, the ratio of equilibrium solubilities is less than 2.0 for 95% of polymorph pairs, based on a selection of 153 polymorph pairs [1]. Exceptionally, in the case of Ritonavir the reported solubility difference was 4-5 fold [2].…”

supporting

confidence: 78%

“…This work follows the report of Abramov that, with some exceptions, the ratio of equilibrium solubilities is less than 2.0 for 95% of polymorph pairs, based on a selection of 153 polymorph pairs [1]. Exceptionally, in the case of Ritonavir the reported solubility difference was 4-5 fold [2].…”

supporting

confidence: 78%

“…where γ is the interfacial energy, k is the Boltzmann constant, N a is Avogadro's number, S is the supersaturation, v m is the molar volume and T is the temperature (in Kelvin). Both the exponential and pre-exponential factors in Equation (1) have been considered by in assessing nucleation rates [8][9][10][11]. For example, Bernstein et al [9] assessed a range of possible nucleation rates for pair of polymorphs at supersaturations where the rate of formation of the metastable polymorph can be identical to that of the stable polymorph, during concomitant polymorph occurrences.…”

mentioning

confidence: 99%

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Thermodynamic vs. Kinetic Basis for Polymorph Selection

Hodnett

Verma

2019

Processes

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Ratios of equilibrium solubilities rarely exceed two-fold for polymorph pairs. A model has been developed based on two intrinsic properties of polymorph pairs, namely the ratio of equilibrium solubilities of the individual pairs (C*me/C*st) and the ratio of interfacial energies (γst/γme) and one applied experimental condition, namely the supersaturation identifies which one of a pair of polymorphs nucleates first. A domain diagram has been developed, which identifies the point where the critical free energy of nucleation for the polymorph pair are identical. Essentially, for a system supersaturated with respect to both polymorphs, the model identifies that low supersaturation with respect to the stable polymorph (Sst) leads to an extremely small supersaturation with respect to the metastable polymorph (Sme), radically driving up the critical free energy with respect to the metastable polymorph. Generally, high supersaturations sometimes much higher than the upper limit of the metastable zone, are required to kinetically favour the metastable polymorph.

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supporting

confidence: 78%

supporting

confidence: 78%

mentioning

confidence: 99%

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Thermodynamic vs. Kinetic Basis for Polymorph Selection

Hodnett

Verma

2019

Processes

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“…30 A potential concern of the physics-based model validation is that solution-mediated, solid-form transformation may take place during the aqueous thermodynamic solubility measurements, while the final solid form in equilibrium with the saturated aqueous solution is rarely characterized. While there is a 95% probability that a solubility ratio between a pair of polymorphs is less than 2-fold, 32 the solubility of a hydrate form may decrease by an order of magnitude relative to that of the corresponding free base form. 33 No information is available in regard to a potential form change during aqueous solubility measurements of BPU and BDZ derivatives.…”

Section: Data Set Selectionmentioning

confidence: 99%

Guiding Lead Optimization for Solubility Improvement with Physics-Based Modeling

Abramov

Sun²,

Zeng³

et al. 2020

Mol. Pharmaceutics

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Although there are a number of computational approaches available for the aqueous solubility prediction, a majority of those models rely on the existence of a training set of thermodynamic solubility measurements or/and fail to accurately account for the lattice packing contribution to the solubility. The main focus of this study is the validation of the application of a physicsbased aqueous solubility approach, which does not rely on any prior knowledge and explicitly describes the solid-state contribution, in order to guide the improvement of poor solubility during the lead optimization. A superior performance of a quantum mechanical (QM)-based thermodynamic cycle approach relative to a molecular mechanical (MM)-based one in application to the optimization of two pharmaceutical series was demonstrated. The QM-based model also provided insights into the source of poor solubility of the lead compounds, allowing the selection of the optimal strategies for chemical modification and formulation. It is concluded that the application of that approach to guide solubility improvement at the late discovery and/or early development stages of the drug design proves to be highly attractive.

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“…It has previously been suggested that it is not essential to calculate lattice energies from the correct polymorph in order to predict solubility [ 49 ] and computational studies suggest most polymorph energies differ by less than 7 kJ/mol [ 50 ]. Nonetheless, the experimental solubilities of polymorphs may differ by around 0.60 log units (log10[molar]) [ 51 ]. (Elsewhere, higher apparent solubility differences between polymorphs are reported, although it is suggested that these differences are typically less than 1.0 log units (log10[molar]) [ 52 ].)…”

Section: Resultsmentioning

confidence: 99%

The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility

Robinson

Martin

2018

J Cheminform

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Predicting the equilibrium solubility of organic, crystalline materials at all relevant temperatures is crucial to the digital design of manufacturing unit operations in the chemical industries. The work reported in our current publication builds upon the limited number of recently published quantitative structure–property relationship studies which modelled the temperature dependence of aqueous solubility. One set of models was built to directly predict temperature dependent solubility, including for materials with no solubility data at any temperature. We propose that a modified cross-validation protocol is required to evaluate these models. Another set of models was built to predict the related enthalpy of solution term, which can be used to estimate solubility at one temperature based upon solubility data for the same material at another temperature. We investigated whether various kinds of solid state descriptors improved the models obtained with a variety of molecular descriptor combinations: lattice energies or 3D descriptors calculated from crystal structures or melting point data. We found that none of these greatly improved the best direct predictions of temperature dependent solubility or the related enthalpy of solution endpoint. This finding is surprising because the importance of the solid state contribution to both endpoints is clear. We suggest our findings may, in part, reflect limitations in the descriptors calculated from crystal structures and, more generally, the limited availability of polymorph specific data. We present curated temperature dependent solubility and enthalpy of solution datasets, integrated with molecular and crystal structures, for future investigations. Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0298-3) contains supplementary material, which is available to authorized users.

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Thermodynamics and Relative Solubility Prediction of Polymorphic Systems

Cited by 7 publications

References 44 publications

Thermodynamic vs. Kinetic Basis for Polymorph Selection

Thermodynamic vs. Kinetic Basis for Polymorph Selection

Guiding Lead Optimization for Solubility Improvement with Physics-Based Modeling

The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility

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