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Manufacture Small Molecules

Moving with the Times

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Small molecules have many advantageous properties, including the ability to permeate via cell membranes to reach their intracellular targets – or not, in the case of the blood-brain barrier, where ingress may not be wanted.

Since the early days of drug development, the needs of the population have changed. As the population ages, human behaviors and lifestyles change – which also affects the types of medicines in demand. Anti-infectives and cardiovascular drugs are obvious products that have improved health and longevity. However, living longer means that neurodegenerative conditions pose an increased burden that requires effective treatment (1). As the demands of a changing society evolve and continue to act as a driver for the identification of novel small molecules, the roles and diversity of the available medicines will also change.

Historically, it has been estimated that approximately 90 percent of all marketed drugs are represented by small molecules. Advances in the technologies available to scientists have seen a rise in the application of biologics, but technology is also impacting the small molecule space. The combined use of in silico screening tools and AI, for example, can create new leads in small molecules. Machine learning algorithms aim to significantly reduce the risk of failure and streamline the optimization process to provide stable, druggable target molecules that are sensible from both a synthetic and a toxicological perspective (3/4). Although machine learning is an exciting advancement in the complex process of developing new medicines, the need for the integration of more traditional medicinal chemistry expertise remains.

Over the past 20 years, there has been a shift in the demographic of those companies taking small molecules into later-stage clinical development. This was a journey undertaken predominantly by big pharmaceutical players, but the number of SMEs that hold on to their assets after phase I has grown. Although the risk of possible failure remains high, the rewards are significant.

Another trend has been a growth in the number of products on an accelerated development pathway, typically because of the unmet needs of a particular patient group. This accelerated trajectory poses a burden on those planning for success as a significant amount of data relating to manufacture, stability, solid form landscape, and formulation are required early in the development timeline to support safe and rapid progression.

Despite the integration of in silico modeling and AI approaches, there are increasing numbers of small molecules entering development that exhibit sub-optimal physicochemical characteristics, such as inadequate solubility or poor permeability. The effort involved in maintaining an appropriate level of efficacy for molecules that demonstrate such issues can be significant. A rather succinct representation of this trend was published in Molecular Pharmaceutics over a decade ago (5).

Just as medicinal chemists may use in silico screening and AI to aid in the design of new structures, the pharmaceutical chemist has access to AI and screening tools to help with the prediction of solvate formation, propensity toward polymorphism, and the likelihood of forming salt or cocrystal versions (6). AI and screening tools represent a growing part of the development toolbox and function to supplement the more traditional screening and manufacturing activities. Perhaps better put, they help streamline those experiments applied and validate the results obtained.

De-risking development

There is no one size fits all when it comes to development. Compounds should be developed on a material basis, as screening and selection should never be formulaic. The entire process must be both iterative and pragmatic. Having the ability to integrate the various aspects of pharmaceutical development, especially in the early phases, is ideal.

Given the pace of early-phase development and the challenging nature of many new chemical entities, having a team of synthetic chemists coordinating with solid state experts enables rapid progression. Taking the time to understand what is important from the beginning will also facilitate the construction of tailored programs. If this is combined with robust understanding and communication of what early formulation strategies might look like, a risk-mitigating development plan can be implemented.

One crucial benefit of integration is easy access to material. As a synthetic process is optimized, the impurity profile changes. If material behavior is brought to the forefront of the initial interactions between the chemist and the solid state scientist, early batches can be profiled with only a few milligrams cost in terms of spent API, forming the foundations of future development. Ideally, this work starts to build a data set that correlates solubility characteristics with solid form and impurity profiles. Solid form characterization normally includes (but is not limited to) crystallography and thermal properties, such as melting point and decomposition temperature.

For BCS class II and IV candidates, this solubility correlation can be of particular significance if an early amorphous batch was positioned toward the lower end of acceptable. Form change to a crystalline (or a more stable crystalline polymorph) would likely reduce efficacy and require a more complex formulation strategy or salt formation, if applicable.

Another benefit of integration is the opportunity to profile each stage of the synthetic process. For highly insoluble molecules, it is likely that, as you progress toward the final structure, solubility will drop. If this is combined with a propensity to polymorphism, control of the critical quality attributes (CQA) of intermediates, as well as the final product, can be more than problematic.

Understanding form change reduces the risk of failure at a later stage, when more is at stake from a production perspective. This issue is particularly evident during early phase batch isolation, when well-designed crystallizations are less common and precipitative methods are more often innocently applied before sufficient data is in hand to understand where additional resources and quality by design are required.

A pragmatic approach to development should enable choices from an early stage and answer the question: “Will a salt be required, or is size reduction the initial option ahead of more complex strategies?” These decisions are critical – and the integration of solid form with chemistry and early pre-formulation activities is a significant benefit to a risk-mitigating program. 

Preformulation evaluations are vital where a molecule has a pKa profile that makes salt formation likely but not without challenge – the main risk being that of facile disproportionation back to the parent. Having a well-characterized batch, solubility data, and a pH solubility profile in aqueous, biorelevant, and common formulation solvent/excipients can make the choice of salt or parent less of a challenge and for very little material cost.

A useful reference to consider is that of Butler and Dressman (7), who created a Developability Classification System (DCS) for oral immediate-release compounds to address the question of what aspects of a molecule’s performance characteristics would limit oral absorption. It can be used to help derive strategies for formulation and the identification of the CQAs of the drug substance that should be the target deliverables from the integrated solid form and chemical development process. 

Small molecules continue to play a pivotal role in developing effective medicines for an aging population. Those molecules that are classified or predicted to sit within BCS class II and IV are of particular significance. However, realizing the benefits of integrating solid form and chemical development teams from an early phase, plus making use of in silico and AI technology, can provide a streamlined and risk-mitigated journey from the early phase to the clinic.

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  1. A Abbot, “Dementia: A problem for our age”, Nature, 475 (2011). DOI: 10.1038/475S2a
  2. HX Ngo, S. Garneau-Tsodikova, “What are the drugs of the future?”, Med. Chem. Commun., 9, 757 (2018). DOI: 10.1039/c8md90019a
  3. A Mullard, “The drug-maker's guide to the galaxy”, Nature, 549, (7673), 445-447 (2017) DOI: 10.1038/549445a/ 
  4. MG Gonzalez, et al., “Oncological drug discovery: AI meets structure-based computational research”, Drug Discovery Today, v27, No6, 1661-1670 (2022), DOI: 10.1016/j.drudis.2022.03.005. 
  5. GL Amidon et al, “A provisional biopharmaceutical classification of the top 200 oral drug products in the United States, Great Britain, Spain, and Japan”, Mol. Pharm, Nov-Dec;3(6):631-43, (2006), DOI: 10.1021/mp0600182
  6. T Heng et al, “Progress in Research on Artificial Intelligence Applied to Polymorphism and Cocrystal Prediction”, ACS Omega, Jun 22; 6(24): 15543–15550, (2021), DOI:10.1021/acsomega.1c01330
  7. JM Butler, JB Dressman, “The developability classification system: application of biopharmaceutics concepts to formulation development”, J. Pharm. Sci.; Dec. 99, (12), 4940-54 (2010), DOI: 10.1002/jps.22217 
About the Author
Julian Northen

Solid State Manager at Onyx Scientific

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