Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization.

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.

How many more polymorphs of ROY remain undiscovered.

With 12 crystal forms, 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecabonitrile (a.k.a. ROY) holds the current record for the largest number of fully characterized organic crystal polymorphs. Four of these polymorph structures have been reported since 2019, raising the question of how many more ROY polymorphs await future discovery. Employing crystal structure prediction and accurate energy rankings derived from conformational energy-corrected density functional theory, this study presents the first crystal energy landscape for ROY that agrees well with experiment. The lattice energies suggest that the seven most stable ROY polymorphs (and nine of the twelve lowest-energy forms) on the Z' = 1 landscape have already been discovered experimentally. Discovering any new polymorphs at ambient pressure will likely require specialized crystallization techniques capable of trapping metastable forms. At pressures above 10 GPa, however, a new crystal form is predicted to become enthalpically more stable than all known polymorphs, suggesting that further high-pressure experiments on ROY may be warranted. This work highlights the value of high-accuracy crystal structure prediction for solid-form screening and demonstrates how pragmatic conformational energy corrections can overcome the limitations of conventional density functionals for conformational polymorphs.

Crystal Structure Prediction Methods for Organic Molecules: State of the Art.

The prediction of the crystal structures that a given organic molecule is likely to form is an important theoretical problem of significant interest for the pharmaceutical and agrochemical industries, among others. As evidenced by a series of six blind tests organized over the past 2 decades, methodologies for crystal structure prediction (CSP) have witnessed substantial progress and have now reached a stage of development where they can begin to be applied to systems of practical significance. This article reviews the state of the art in general-purpose methodologies for CSP, placing them within a common framework that highlights both their similarities and their differences. The review discusses specific areas that constitute the main focus of current research efforts toward improving the reliability and widening applicability of these methodologies, and offers some perspectives for the evolution of this technology over the next decade.

Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. II. Smoothed intramolecular potentials.

The application of crystal structure prediction (CSP) to industrially relevant molecules requires the handling of increasingly large and flexible compounds. A revised model for the effect of molecular flexibility on the lattice energy that removes the discontinuities and non-differentiabilities present in earlier models (Sugden et al., 2016), with a view to improving the performance of CSP is presented. The approach is based on the concept of computing a weighted average of local models, and has been implemented within the CrystalPredictor code. Through the comparative investigation of several compounds studied in earlier literature, it is shown that this new model results in large reductions in computational effort (of up to 65%) and in significant increases in reliability. The approach is further applied to investigate, for the first time, the computational polymorphic landscape of flufenamic acid for Z' = 1 structures, resulting in the successful identification of all three experimentally resolved polymorphs within reasonable computational time.

Repulsion-dispersion parameters for the modelling of organic molecular crystals containing N, O, S and Cl.

In lattice energy models that combine ab initio and empirical components, it is important to ensure consistency between these components so that meaningful quantitative results are obtained. A method for deriving parameters of atom-atom repulsion dispersion potentials for crystals, tailored to different ab initio models, is presented. It is based on minimization of the sum of squared deviations between experimental and calculated structures and energies. The solution algorithm is designed to avoid convergence to local minima in the parameter space by combining a deterministic low-discrepancy sequence for the generation of multiple initial parameter guesses with an efficient local minimization algorithm. The proposed approach is applied to derive transferable exp-6 potential parameters suitable for use in conjunction with a distributed multipole electrostatics model derived from isolated molecule charge densities calculated at the M06/6-31G(d,p) level of theory. Data for hydrocarbons, azahydrocarbons, oxohydrocarbons, organosulphur compounds and chlorohydrocarbons are used for the estimation. A good fit is achieved for the new set of parameters with a mean absolute error in sublimation enthalpies of 4.1 kJ mol-1 and an average rmsd15 of 0.31 Å. The parameters are found to perform well on a separate cross-validation set of 39 compounds.

Accurate and efficient representation of intramolecular energy in ab initio generation of crystal structures. I. Adaptive local approximate models.

The global search stage of crystal structure prediction (CSP) methods requires a fine balance between accuracy and computational cost, particularly for the study of large flexible molecules. A major improvement in the accuracy and cost of the intramolecular energy function used in the CrystalPredictor II [Habgood et al. (2015). J. Chem. Theory Comput. 11, 1957-1969] program is presented, where the most efficient use of computational effort is ensured via the use of adaptive local approximate model (LAM) placement. The entire search space of the relevant molecule's conformations is initially evaluated using a coarse, low accuracy grid. Additional LAM points are then placed at appropriate points determined via an automated process, aiming to minimize the computational effort expended in high-energy regions whilst maximizing the accuracy in low-energy regions. As the size, complexity and flexibility of molecules increase, the reduction in computational cost becomes marked. This improvement is illustrated with energy calculations for benzoic acid and the ROY molecule, and a CSP study of molecule (XXVI) from the sixth blind test [Reilly et al. (2016). Acta Cryst. B72, 439-459], which is challenging due to its size and flexibility. Its known experimental form is successfully predicted as the global minimum. The computational cost of the study is tractable without the need to make unphysical simplifying assumptions.

Efficient Handling of Molecular Flexibility in Ab Initio Generation of Crystal Structures.

A key step in many approaches to crystal structure prediction (CSP) is the initial generation of large numbers of candidate crystal structures via the exploration of the lattice energy surface. By using a relatively simple lattice energy approximation, this global search step aims to identify, in a computationally tractable manner, a limited number of likely candidate structures for further refinement using more detailed models. This paper presents an effective and efficient approach to modeling the effects of molecular flexibility during this initial global search. Local approximate models (LAMs), constructed via quantum mechanical (QM) calculations, are used to model the conformational energy, molecular geometry, and atomic charge distributions as functions of a subset of the conformational degrees of freedom (e.g., flexible torsion angles). The effectiveness of the new algorithm is demonstrated via its application to the recently studied 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile (ROY) molecule and to two molecules, ß-D-glucose and 1-(4-benzoylpiperazin-1-yl)-2-(4,7-dimethoxy-1H-pyrrolo[2,3-c]pyridin-3-yl)ethane-1,2-dione, a Bristol Myers Squibb molecule referenced as BMS-488043. All three molecules present significant challenges due to their high degree of flexibility.

General computational algorithms for ab initio crystal structure prediction for organic molecules.

The prediction of the possible crystal structure(s) of organic molecules is an important activity for the pharmaceutical and agrochemical industries, among others, due to the prevalence of crystalline products. This chapter considers the general requirements that crystal structure prediction (CSP) methodologies need to fulfil in order to be able to achieve reliable predictions over a wide range of organic systems. It also reviews the current status of a multistage CSP methodology that has recently proved successful for a number of systems of practical interest. Emphasis is placed on recent developments that allow a reconciliation of conflicting needs for, on the one hand, accurate evaluation of the energy of a proposed crystal structure and on the other hand, comprehensive search of the energy landscape for the reliable identification of all low-energy minima. Finally, based on the experience gained from this work, current limitations and opportunities for further research in this area are identified. We also consider issues relating to the use of empirical models derived from experimental data in conjunction with ab initio CSP.

The polymorphs of ROY: application of a systematic crystal structure prediction technique.

We investigate the ability of current ab initio crystal structure prediction techniques to identify the polymorphs of 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile, also known as ROY because of the red, orange and yellow colours of its polymorphs. We use a methodology combining the generation of a large number of structures based on a computationally inexpensive model using the CrystalPredictor global search algorithm, and the further minimization of the most promising of these structures using the CrystalOptimizer local minimization algorithm which employs an accurate, yet efficiently constructed, model based on isolated-molecule quantum-mechanical calculations. We demonstrate that this approach successfully predicts the seven experimentally resolved structures of ROY as lattice-energy minima, with five of these structures being within the 12 lowest energy structures predicted. Some of the other low-energy structures identified are likely candidates for the still unresolved polymorphs of this molecule. The relative stability of the predicted structures only partially matches that of the experimentally resolved polymorphs. The worst case is that of polymorph ON, whose relative energy with respect to Y is overestimated by 6.65 kJ mol(-1). This highlights the need for further developments in the accuracy of the energy calculations.

Towards crystal structure prediction of complex organic compounds--a report on the fifth blind test.

Following on from the success of the previous crystal structure prediction blind tests (CSP1999, CSP2001, CSP2004 and CSP2007), a fifth such collaborative project (CSP2010) was organized at the Cambridge Crystallographic Data Centre. A range of methodologies was used by the participating groups in order to evaluate the ability of the current computational methods to predict the crystal structures of the six organic molecules chosen as targets for this blind test. The first four targets, two rigid molecules, one semi-flexible molecule and a 1:1 salt, matched the criteria for the targets from CSP2007, while the last two targets belonged to two new challenging categories - a larger, much more flexible molecule and a hydrate with more than one polymorph. Each group submitted three predictions for each target it attempted. There was at least one successful prediction for each target, and two groups were able to successfully predict the structure of the large flexible molecule as their first place submission. The results show that while not as many groups successfully predicted the structures of the three smallest molecules as in CSP2007, there is now evidence that methodologies such as dispersion-corrected density functional theory (DFT-D) are able to reliably do so. The results also highlight the many challenges posed by more complex systems and show that there are still issues to be overcome.

Successful prediction of a model pharmaceutical in the fifth blind test of crystal structure prediction.

The range of target structures in the fifth international blind test of crystal structure prediction was extended to include a highly flexible molecule, (benzyl-(4-(4-methyl-5-(p-tolylsulfonyl)-1,3-thiazol-2-yl)phenyl)carbamate, as a challenge representative of modern pharmaceuticals. Two of the groups participating in the blind test independently predicted the correct structure. The methods they used are described and contrasted, and the implications of the capability to tackle molecules of this complexity are discussed.

Can the Formation of Pharmaceutical Cocrystals Be Computationally Predicted? 2. Crystal Structure Prediction.

We report a multistage lattice energy minimization methodology for generating stable packing arrangements of cocrystals containing flexible molecules. In the first approximation, the intermolecular electrostatic interactions are modeled with atomic charges and the molecular deformation energy is interpolated over a set of precomputed quantum mechanical values. At subsequent stages, the accuracy is improved by first using analytically rotated and then conformation-dependent multipole moments, computed from the isolated-molecule charge density, and "on-the-fly" quantum mechanical calculations to compute the intramolecular deformation energy. This multistage approach increases the efficiency of the search and establishes the molecule-dependent error due to the atomic charge representation of the charge density and the neglect of the conformational dependence of atomic multipole moments. The methodology is used to study the lattice energy landscapes of the cocrystals of 4-aminobenzoic acid with 2,2'-bipyridine and 4-nitrophenylacetic acid, as well as the single-component crystals. All single-component, experimentally determined crystal structures within the scope of the search were found at, or very close to, the global minimum. The experimental cocrystal with 2,2'-bipyridine is also predicted to be among the most stable packing arrangements. On the contrary, the lattice energy landscape of the cocrystal with 4-nitrophenylacetic acid contains several low energy structures that are more stable than the experimentally observed form and have different hydrogen bonding motifs. Overall, the methodology can provide worthwhile crystal energy landscapes for multicomponent organic solids and thereby contribute to understanding cocrystal formation.

Ab initio crystal structure prediction-I. Rigid molecules.

A new methodology for the prediction of molecular crystal structures using only the atomic connectivity of the molecule under consideration is presented. The approach is based on the global minimization of the lattice enthalpy of the crystal. The modeling of the electrostatic interactions is accomplished through a set of distributed charges that are optimally and automatically selected and positioned based on results of quantum mechanical calculations. A four-step global optimization algorithm is used for the identification of the local minima of the lattice enthalpy surface. A parallelized implementation of the algorithm permits a much more extensive search of the solution space than has hitherto been possible, allowing the identification of crystal structures in less frequently occurring space groups and with more than one molecule in the asymmetric unit. The algorithm has been applied successfully to the prediction of the crystal structures of 3-aza-bicyclo(3.3.1)nonane-2,4-dione (P2(1)/a, Z' = 1), allopurinol (P2(1)/c, Z' = 1), 1,3,4,6,7,9-hexa-azacycl(3.3.3)azine (Pbca, Z' = 2), and triethylenediamine (P6(3)/m, Z' = 1). In all cases, the experimentally known structure is among the most stable predicted structures, but not necessarily the global minimum.