Virtual coformer screening by a combined machine learning and physics-based approach

Yuan, Jiuchuang; Liu, Xuetao; Wang, Simin; Chang, Chao; Zeng, Qiao; Song, Zhengtian; Jin, Yingdi; Zeng, Qun; Sun, Geng; Ruan, Shigang; Greenwell, Chandler; Abramov, Yuriy A.

doi:10.1039/d1ce00587a

Cited by 18 publications

(20 citation statements)

References 57 publications

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“…52,53 A similar approach has also been undertaken with DFT-d methods and proven successful. 25 However, the key advantage of the proposed approach is its computational efficiency arising from the use of the CrystalPredictor 30 / CrystalOptimizer 31 framework for the CSP studies at steps 1, 3a, and 3c of the algorithm in an integrated manner.…”

Section: Proposed Approach To Computationalmentioning

confidence: 99%

“…We note that similar methodologies have also been used in recent investigations into three-component ionic cocrystals, solvates (with the energy costs for converting a liquid solvent molecule to a solid accounted for with a 3/2 RT term), and hydrates. , A similar approach has also been undertaken with DFT-d methods and proven successful . However, the key advantage of the proposed approach is its computational efficiency arising from the use of the CrystalPredictor/CrystalOptimizer framework for the CSP studies at steps 1, 3a, and 3c of the algorithm in an integrated manner.…”

Section: Proposed Approach To Computational Coformer Screeningmentioning

confidence: 99%

“…Thus, the de novo generation of a computational landscape for a given pair of compounds with state-of-the-art methods can often take hundreds of thousands or even millions of CPU hours. 25,26 Another challenge for the application of CSP methods to cocrystals relates to the derivation of sufficiently accurate lattice energy models (force fields) for a relatively large number of API + coformer pairs. In some CSP methods, such as GRACE 27 or XtalPi, 28 the force field is tailored to the system under consideration by carrying out periodic dispersion-corrected density functional theory (DFT-d) calculations 29 on a set of crystal structures.…”

Section: Introductionmentioning

confidence: 99%

“…CSP methods have already been used successfully to study cocrystals in several investigations. ,− Cocrystals are particularly challenging in this context because introducing more molecules into the crystal’s asymmetric unit increases the number of degrees of freedom that need to be explored in identifying all low-energy crystal structures. Thus, the de novo generation of a computational landscape for a given pair of compounds with state-of-the-art methods can often take hundreds of thousands or even millions of CPU hours. , …”

Section: Introductionmentioning

confidence: 99%

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Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Sugden

Braun

Bowskill

et al. 2022

Crystal Growth & Design

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Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know a priori which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API−coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a cis-aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.

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Section: Proposed Approach To Computationalmentioning

confidence: 99%

Section: Proposed Approach To Computational Coformer Screeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Sugden

Braun

Bowskill

et al. 2022

Crystal Growth & Design

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“…In addition, a combined physic-based and ML approach, ML+COSMO-RS, was recently proposed, which proved to be significantly better than the sum of its parts. 141 Despite many successful applications, most of the virtual coformer screening methods suffer from a lack or poor description of the crystallinity (long-range order) contribution to cocrystallization. That limitation is successfully addressed by the CSP(-Lite)-based virtual coformer screening calculations to guide cocrystallization design.…”

Section: Physics-based Virtual Screening Approaches To Support Solid ...mentioning

confidence: 99%

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Abramov

Sun²,

Zeng³

2022

J. Chem. Inf. Model.

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Computational chemistry applications have become an integral part of the drug discovery workflow over the past 35 years. However, computational modeling in support of drug development has remained a relatively uncharted territory for a significant part of both academic and industrial communities. This review considers the computational modeling workflows for three key components of drug preclinical and clinical development, namely, process chemistry, analytical research and development, as well as drug product and formulation development. An overview of the computational support for each step of the respective workflows is presented. Additionally, in context of solid form design, special consideration is given to modern physics-based virtual screening methods. This covers rational approaches to polymorph, coformer, counterion, and solvent virtual screening in support of solid form selection and design.

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Absolute Configuration Determination of Chiral API Molecules by MicroED Analysis of Cocrystal Powders Formed Based on Cocrystal Propensity Prediction Calculations**

Shah

Yuan²,

Xie

et al. 2023

Chemistry A European J

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Establishing the absolute configuration of chiral active pharmaceutical ingredients (APIs) is of great importance. Single crystal X-ray diffraction (scXRD) has traditionally been the method of choice for such analysis, but scXRD requires the growth of large crystals, which can be challenging. Here, we present a method for determining absolute configuration that does not rely on the growth of large crystals. By examining microcrystals formed with chiral probes (small chiral compounds such as amino acids), absolute configuration can be unambiguously determined by microcrystal electron diffraction (MicroED). Our streamlined method employs three steps: (1) virtual screening to identify promising chiral probes, (2) experimental cocrystal screening and (3) structure determination by MicroED and absolute configuration assignment. We successfully applied this method to analyze two chiral API molecules currently on the market for which scXRD was not used to determine absolute configuration.

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Virtual coformer screening by a combined machine learning and physics-based approach

Abstract: Cocrytals as a solid form technology for improving physicochemical properties have grown increasing popular in the pharmaceutical, nutraceutical, and agrochemical industries. However, the list of potential coformers contains hundreds of...

Cited by 18 publications

References 57 publications

Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Absolute Configuration Determination of Chiral API Molecules by MicroED Analysis of Cocrystal Powders Formed Based on Cocrystal Propensity Prediction Calculations**

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