Applying Bioisosteric Transformations to Predict Novel, High Quality Compounds Dr James Chisholm,* Dr John Barnard, Dr Julian Hayward, Dr Matthew Segall*, Mr Edmund Champness*, Dr Chris Leeding,* Mr Hector Martinez,* Mr Iskander Yusof* * Optibrium Ltd., 7221 Cambridge Research Park, Beach Drive, Cambridge, CB25 9TL, UK Digital Chemistry Ltd., 30 Kiveton Lane, Todwick, Sheffield, S26 1HL, UK Bioisosteres are functional groups which have similar physical or chemical characteristics and hence similar biological effects.[1] The principle of bioisosterism is widely used in drug discovery, where it is often desirable to maintain similar molecular interactions to retain target potency, while accessing new chemical space. This may be required in order to overcome issues related to absorption, distribution, metabolism, elimination, or toxicity (ADMET) properties or to find novel chemical matter to expand or avoid the scope of a patent. There are many examples of bioisosteric replacements in the literature,[2] but it is challenging to identify those that may be applicable to a chemistry of interest and assess the potential results of making a similar modification. A recent development in cheminformatics is the concept of idea generation to speed up the exploration of chemical space [3] [4] around a compound of interest. These approaches apply medicinal chemistry transformations to an initial compound to generate new, related compound ideas. These transformations typically represent generally applicable compound modifications commonly considered by medicinal chemists in compound optimization. These two concepts can be combined to automatically find and apply bioisosteric replacements to generate novel compound structures that are likely to preserve the required biological activities. The properties of the resulting compounds can be predicted and the most promising ideas prioritized for further consideration using multi-parameter optimization (MPO) [5], to identify those that are most likely to have a good balance of the properties required in a high-quality drug. Creating bioisosteric transformations To generate a database of molecular transformations, we used the BIOSTER database, a compilation of 27,366 pairs of molecules with bioisosteric substructures, manually curated from the scientific literature. Each BIOSTER record is in the form of a pseudo reaction relating the two compounds, with a manually designated reaction center indicating the bioisosteric substructures (see Figure 1).
Figure 1. Illustration of the process of generating a transformation from a pair of bioisosteric compounds. At the top, the initial pair of compounds is shown, with the bioisosteric replacement highlighted as hashed bonds. The points of substitution on these compounds are used to define the mapped atoms in the transformation, resulting in the SMIRKS string shown at the bottom. A transformation was generated for each record, representing the atoms in the bioisosteric substructures in Daylight s SMIRKS notation (which appropriate software can apply to initial
molecules to suggest new ones) using a customized version of Digital Chemistry's MOLSMART program. A number of challenges needed to be overcome to generate good transformations. For example, substituent groups around the substructures are not necessarily identical in both molecules, and equivalent substitution positions on the reactant and product sides had to be identified heuristically and mapped appropriately. In addition, in order to minimize the number of promiscuous transforms that could be applied inappropriately and generate structures that do not make sense from a medicinal chemistry perspective, substitution was permitted only where there was a substituent on at least one side of the reaction, and atoms were restricted to the ring/chain and aliphatic/aromatic environment found in the original molecules. Of the 27,366 records in the latest version of the BIOSTER database SMIRKS were successfully generated for 22,547 (82.4%). Multi-parameter optimization A high-quality lead or drug candidate must achieve a balance of many, often conflicting properties, including potency and ADMET properties. MPO methods integrate data on multiple properties to efficiently identify chemistries that are most likely to achieve an appropriate balance for a drug discovery project s therapeutic objective [5]. In the examples presented herein, the compound ideas generated were prioritized using a probabilistic scoring algorithm that assesses the properties of a compound against the ideal property profile for the project [8]. Example profiles are shown in Figure 2, illustrating how a project can define the desired outcome for each property and the importance of each criterion to the overall project objective. In this way, the profile reflects the acceptable trade-offs between different properties. A score is calculated for each compound reflecting its likelihood of achieving the ideal property profile, taking into account the uncertainty in the individual property predictions. (a) Figure 2. Example scoring profiles: (a) defines appropriate ADMET properties for an orally dosed compound for a central nervous system (CNS) target; (b) defines an appropriate balance of properties for a potent inhibitor intended for a peripheral target, in this case the Histamine H1 receptor MPO methods can be applied to both experimental and predicted compound data, but this application prioritizes virtual compound ideas. Therefore, the prioritization must be based on predictions from in silico models. (b)
Predictive application of bioisosteric transformations In the following two retrospective examples, the bioisosteric transformations were applied using the Nova [4] module and prioritized using in silico models of ADMET properties and the probabilistic scoring method provided by the StarDrop software platform [9]. Lead optimization: dipeptidyl peptidase IV inhibitor The BIOSTER transformations were applied to the lead compound from the project that resulted in the discovery of the anti-diabetic dipeptidyl peptidase IV (DPP IV) inhibitor alogliptin [10]. This resulted in the generation of 230 compounds that were prioritized against the scoring profile shown in Figure 2 and some illustrative results are shown in Figure 3. It is notable that the product shown in the center of Figure 3 is a close analogue of alogliptin (also shown in Figure 3 for comparison). Figure 3. Illustrative examples of the application of the BIOSTER transformations to a lead compound from which the DPP IV inhibitor Alogliptin was discovered. The scores for each compound were generated using the scoring profile shown in Figure 2(a); the colours in the histograms correspond to the key shown in this figure and show the impact of each property on the overall score. The structure of Alogliptin is shown for comparison. Fast follower: histamine H1 receptor antagonist Application of the BIOSTER transformations to the antihistamine drug azatadine yielded a total of 89 compounds that were prioritized against the profile shown in Figure 2b, including pk i against the histamine H1 receptor, predicted using a QSAR model. Some illustrative results are shown in Figure 4 and it is notable that the product on the right represents the core replacement that led to the candidate compound Hivenyl [11] (see Figure 4).
Figure 4. Illustrative examples of the application of the BIOSTER transformations to the drug Azatadine (top). The scores for each compound were generated using the scoring profile shown in Figure 2(b); the colours in the histograms correspond to the key shown in this figure and show the impact of each property on the overall score. The structure of Hivenyl is shown for comparison. Conclusions Bioisosteric transformations are an excellent source of new ideas for compound design, providing access to increased chemical diversity while maintaining a high likelihood of biological activity. Automatically applying bioisisteric transformations from a large database of precedented replacements enables efficient exploration of new chemical space in the search for new optimization strategies. This may result in a large number of new ideas, which can be prioritized to highlight those most likely to succeed against a project s objectives. Furthermore, links to the primary literature, from which the transformations were derived, make it easy to follow-up the most interesting ideas to find synthetic routes and investigate the underlying biological data. This approach can be applied throughout the drug discovery process, including expansion around initial hits, exploring scaffold hopping opportunities in lead optimization and patent protection.
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