FDA/M-CERSI Co-Processed API Workshop Proceedings

These proceedings contain presentation summaries and discussion highlights from the University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI) Workshop on Co-processed API, held on July 13 and 14, 2022. This workshop examined recent advances in the use of co-processed active pharmaceutical ingredients as a technology to improve drug substance physicochemical properties and drug product manufacturing process robustness, and explored proposals for enabling commercialization of these transformative technologies. Regulatory considerations were discussed with a focus on the classification, CMC strategies, and CMC documentation supporting the use of this class of materials from clinical studies through commercialization. The workshop format was split between presentations from industry, academia and the FDA, followed by breakout sessions structured to facilitate discussion. Given co-processed API is a relatively new concept, the authors felt it prudent to compile these proceedings to gain further visibility to topics discussed and perspectives raised during the workshop, particularly during breakout discussions. Disclaimer: This paper reflects discussions that occurred among stakeholder groups, including FDA, on various topics. The topics covered in the paper, including recommendations, therefore, are intended to capture key discussion points. The paper should not be interpreted to reflect alignment on the different topics by the participants, and the recommendations provided should not be used in lieu of FDA published guidance or direct conversations with the Agency about a specific development program. This paper should not be construed to represent FDA's views or policies.

Cocrystal Prediction of Bexarotene by Graph Convolution Network and Bioavailability Improvement

Bexarotene (BEX) was approved by the FDA in 1999 for the treatment of cutaneous T-cell lymphoma (CTCL). The poor aqueous solubility causes the low bioavailability of the drug and thereby limits the clinical application. In this study, we developed a GCN-based deep learning model (CocrystalGCN) for in-silico screening of the cocrystals of BEX. The results show that our model obtained high performance relative to baseline models. The top 30 of 109 coformer candidates were scored by CocrystalGCN and then validated experimentally. Finally, cocrystals of BEX-pyrazine, BEX-2,5-dimethylpyrazine, BEX-methyl isonicotinate, and BEX-ethyl isonicotinate were successfully obtained. The crystal structures were determined by single-crystal X-ray diffraction. Powder X-ray diffraction, differential scanning calorimetry, and thermogravimetric analysis were utilized to characterize these multi-component forms. All cocrystals present superior solubility and dissolution over the parent drug. The pharmacokinetic studies show that the plasma exposures (AUC0-8h) of BEX-pyrazine and BEX-2,5-dimethylpyrazine are 1.7 and 1.8 times that of the commercially available BEX powder, respectively. This work sets a good example for integrating virtual prediction and experimental screening to discover the new cocrystals of water-insoluble drugs.

Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

Artificial intelligence and specifically machine learning applications are nowadays used in a variety of scientific applications and cutting-edge technologies, where they have a transformative impact. Such an assembly of statistical and linear algebra methods making use of large data sets is becoming more and more integrated into chemistry and crystallization research workflows. This review aims to present, for the first time, a holistic overview of machine learning and cheminformatics applications as a novel, powerful means to accelerate the discovery of new crystal structures, predict key properties of organic crystalline materials, simulate, understand, and control the dynamics of complex crystallization process systems, as well as contribute to high throughput automation of chemical process development involving crystalline materials. We critically review the advances in these new, rapidly emerging research areas, raising awareness in issues such as the bridging of machine learning models with first-principles mechanistic models, data set size, structure, and quality, as well as the selection of appropriate descriptors. At the same time, we propose future research at the interface of applied mathematics, chemistry, and crystallography. Overall, this review aims to increase the adoption of such methods and tools by chemists and scientists across industry and academia.

Prediction of Mefenamic Acid Crystal Shape by Random Forest Classification

OBJECTIVE

Particle shape can have a significant impact on the bulk properties of materials. This study describes the development and application of machine-learning models to predict the crystal shape of mefenamic acid recrystallized from organic solvents.

METHODS

Crystals were grown in 30 different solvents to establish a dataset comprising solvent molecular descriptors, process conditions and crystal shape. Random forest classification models were trained on this data and assessed for prediction accuracy.

RESULTS

The highest prediction accuracy of crystal shape was 93.5% assessed by fourfold cross-validation. When solvents were sequentially excluded from the training data, 32 out of 84 models predicted the shape of mefenamic acid crystals for the excluded solvent with 100% accuracy and a further 21 models had prediction accuracies from 50-100%. Reducing the feature set to only solvent physical property descriptors and supersaturations resulted in higher overall prediction accuracies than the models trained using all available or another selected subset of molecular descriptors. For the 8 solvents on which the models performed poorly (< 50% accuracy), further characterisation of crystals grown in these solvents resulted in the discovery of a new mefenamic acid solvate whereas all other crystals were the previously known form I.

CONCLUSIONS

Random forest classification models using solvent physical property descriptors can reliably predict crystal morphologies for mefenamic acid crystals grown in 20 out of the 28 solvents included in this work. Poor prediction accuracies for the remaining 8 solvents indicate that further factors will be required in the feature set to provide a more generalized predictive morphology model.

The Ambiguous Functions of the Precursors That Enable Nonclassical Modes of Olanzapine Nucleation and Growth

One of the most consequential assumptions of the classical theories of crystal nucleation and growth is the Szilard postulate, which states that molecules from a supersaturated phase join a nucleus or a growing crystal individually. In the last 20 years, observations in complex biological, geological, and engineered environments have brought to light violations of the Szilard rule, whereby molecules assemble into ordered or disordered precursors that then host and promote nucleation or contribute to fast crystal growth. Nonclassical crystallization has risen to a default mode presumed to operate in the majority of the inspected crystallizing systems. In some cases, the existence of precursors in the growth media is admitted as proof for their role in nucleation and growth. With the example of olanzapine, a marketed drug for schizophrenia and bipolar disorder, we demonstrate that molecular assemblies in the solution selectively participate in crystal nucleation and growth. In aqueous and organic solutions, olanzapine assembles into both mesoscopic solute-rich clusters and dimers. The clusters facilitate nucleation of crystals and crystal form transformations. During growth, however, the clusters land on the crystal surface and transform into defects, but do not support step growth. The dimers are present at low concentrations in the supersaturated solution, yet the crystals grow by the association of dimers, and not of the majority monomers. The observations with olanzapine emphasize that detailed studies of the crystal and solution structures and the dynamics of molecular association may empower classical and nonclassical models that advance the understanding of natural crystallization, and support the design and manufacture of promising functional materials.

Progress in Research on Artificial Intelligence Applied to Polymorphism and Cocrystal Prediction

Artificial intelligence (AI) is a technology that builds an artificial system with certain intelligence and uses computer software and hardware to simulate intelligent human behavior. When combined with drug research and development, AI can considerably shorten this cycle, improve research efficiency, and minimize costs. The use of machine learning to discover novel materials and predict material properties has become a new research direction. On the basis of the current status of worldwide research on the combination of AI and crystal form and cocrystal, this mini-review analyzes and explores the application of AI in polymorphism prediction, crystal structure analysis, crystal property prediction, cocrystal former (CCF) screening, cocrystal composition prediction, and cocrystal formation prediction. This study provides insights into the future applications of AI in related fields.

Crystal forms in pharmaceutical applications: olanzapine, a gift to crystal chemistry that keeps on giving

This contribution reviews the efforts of many scientists around the world to discover and structurally characterize olanzapine crystal forms, clearing up inconsistencies in the scientific and patent literature and highlighting the challenges in identifying new forms amidst 60+ known polymorphs and solvates. Owing to its remarkable solid-state chemistry, olanzapine has emerged over the last three decades as a popular tool compound for developing new experimental and computational methods for enhanced molecular level understanding of solid-state structure, form diversity and crystallization outcomes. This article highlights the role of olanzapine in advancing the fundamental understanding of crystal forms, interactions within crystal structures, and growth units in molecular crystallization, as well as influencing the way in which drugs are developed today.

A Simultaneous Differential Scanning Calorimetry-X-ray Diffraction Study of Olanzapine Crystallization from Amorphous Solid Dispersions

Amorphous solid dispersions (ASDs) of class II and IV biopharmaceutics classification system drugs in water-miscible polymers are a well-recognized means of enhancing dissolution, while such dispersions in hydrophobic polymers form the basis of micro- and nanoparticulate technologies. However, drug recrystallization presents significant problems for product development, and the mechanisms and pathways involved are poorly understood. Here, we outline the use of combined differential scanning calorimetry (DSC)-synchrotron X-ray diffraction to monitor the sequential appearance of polymorphs of olanzapine (OLZ) when dispersed in a range of polymers. In a recent study (Cryst. Growth Des. 2019, 19, 2751-2757), we reported a new polymorph (form IV) of OLZ which crystallized from a spray-dried dispersion of OLZ in polyvinylpyrrolidone. Here, we extend our earlier study to explore OLZ dispersions in poly(lactide-co-glycolide) (PLGA), polylactide (PLA), and hydroxypropyl methyl cellulose acetate succinate (HPMCAS), with a view to identifying the sequence of form generation on heating each dispersion. While spray-dried OLZ results in the formation of crystalline form I, the spray-dried material with HPMCAS comprises an ASD, and forms I and IV are generated upon heating. PLGA and PLA result in a product which contains both amorphous OLZ and the dichloromethane solvate; upon heating, the amorphous material converts to forms I, II, and IV and the solvate to forms I and II. Our data show that it is possible to quantitatively assess not only the polymorph generation sequence but also the relative proportions as a function of temperature. Of particular note is that the sequence of form generation is significantly more complex than may be indicated by DSC data alone, with coincident generation of different polymorphs and complex interconversions as the material is heated. We argue that this may have implications not only for the mechanistic understanding of polymorph generation but also as an aid to identifying the range of polymorphic forms that may be produced by a single-drug molecule.

Influence of crystal packing on the thermal properties of cocrystals and cocrystal solvates of olanzapine: insights from computations

A multicomponent supramolecular host with adaptive guest accommodation abilities is observed in the cocrystal solvates of the olanzapine–hydroquinone system.

A review on possible mechanisms of sonocrystallisation in solution

Sonocrystallisation is the application of ultrasound to the crystallisation process. The benefits obtained by sonication have been widely studied since the beginning of the 20th century and so far it is clear that ultrasound can be a very useful tool for enhancing crystallisation and controlling the properties of the final product. Crystal size, polymorphs, purity, process repeatability and lower induction time are only some of the advantages of sonocrystallisation. Even though the effects of sonication on crystallisation are quite clear, the physical explanation of the phenomena involved is still lacking. Is the presence of cavitation necessary for the process? Or is only the bubbles surface responsible for enhancing crystallisation? Are the strong local increases in pressure and temperature induced by cavitation the main cause of all the observed effects? Or is it the strong turbulence induced in the system instead? Many questions still remain and can only be appreciated with an understanding of the complexity behind the individual processes of crystallisation and acoustic cavitation. Therefore, this review will first summarise the theories behind crystallisation and acoustic cavitation, followed by a description of all the current proposed sonocrystallisation mechanisms, and conclude with an overview on future prospects of sonocrystallisation applications.

From serendipity to supramolecular design: assessing the utility of computed crystal form landscapes in inferring the risks of crystal hydration in carboxylic acids

Calculated structural descriptors for predicted anhydrate polymorphs are used to assess the risks of crystal hydration in carboxylic acids.