Improving the Success of Lead Optimization with High-Quality Data and in Silico Tools

ARTICLE Improving the Success of Lead Optimization with High-Quality Data and In Silico Tools

REDUCING FAILURE AT THE MOST EXPENSIVE PRECLINICAL STAGE The combination of high-quality, accessible data and powerful in silico profiling solutions has proven to greatly improve the success of lead optimization. An increasing number of pharmaceutical companies and their partners are embracing this approach. Not only can this combination accurately predict the potential of lead compounds, it is far less expensive than traditional methods and allows the coverage of a broader range of NMEs. The new model of pharmaceutical development requires ready access to relevant data and in silico profiling tools.

In the past, the model for maintaining a profitable pharmaceutical company was relatively straightforward: advance promising compounds through the clinical trials pipeline with an aim to being either first-in-class or significantly more effective than other therapies, then ride this blockbuster to provide profits and to fund the next big thing. Today, as the last generation of blockbuster drugs go off-patent, the cost of bringing a new drug to market continues to rise, currently lying somewhere between $1.6 and $2.6 billion depending on who you ask (1,2). These costs and the significantly lower annual sales for approved drugs have put significant pressure on the industry to create a new business model that reduces the costs of drug development by significantly increasing the success rate of new molecular entities (NMEs) advanced to clinical trials. Nowhere is this more apparent than in the preclinical phase of drug development. “Failing candidates early” has become the battle cry of reducing costs, but deciding which candidates to advance to clinical trials is the reverse of it. Lead optimization means identifying promising NMEs and finding ways to alter their composition to fine-tune potency and pharmacokinetics, and reduce off-target effects. It consumes the largest share of preclinical dollars, exceeding the costs of target-to-hit, hit-to-lead and preclinical work combined (3). DATA EMPOWERS LEAD OPTIMIZATION Much of the work of lead optimization is focused on reducing off-target effects. Traditional AMDET work focused on in vitro and in vivo studies and data modeling methods (e.g., QSARs) that use molecular properties and experimental data to model complex biological processes. The shortcoming of data modeling used to be a lack of high-quality data. Now, expertly curated and normalized data is more readily available. Furthermore, tools for assessing the data and performing highly accurate in silico profiling have been developed to an incredibly powerful state. In silico modeling can predict the ADMET properties of compounds and has the benefit of being far less expensive than traditional methods. A researcher can query a broad number of potential NME structures in less time. Finally, in silico methods help pharmaceutical companies greatly decrease their reliance on animal studies. As the industry looks to move NMEs more efficiently through the development pipeline, it can significantly benefit from published data on chemical compounds from journals, patents and patent applications, textbooks and conference proceedings. Leveraging a comprehensive source of publicly available data can help select the most promising candidates. Easy access to these data enables medicinal chemists to identify and create the structures most likely to have optimal therapeutic properties and a favorable ADMET profile. How researchers can pinpoint the most relevant data for lead optimization is equally important. Even if relevant data exist, they are of no use if they cannot quickly and easily be found. For a lead optimization team to identify chemical entities with similar structures, they need a variety of options to create queries.

2 Having a variety of ways to query a database ensures researchers do not drown in a deluge of information. They can structure their queries to retrieve exact answers and accelerate the workflow. There is no reason to read full-text articles in the hope of finding a single fact. IN SILICO MODELING ADDS VALUE TO THE DATA The drive to become more efficient has moved companies from a volume approach to one that seeks to have a targeted, smaller library aimed at generating more valuable data when the compounds are moved to in vitro and in vivo studies. In silico tools that can use the public domain data for a compound or related compounds together with a company’s experimental data on their NME are ideal for comparative studies of physicochemical and biochemical properties. Lead optimization can often add potentially undesirable characteristics to the NME, including increasing molecular weight or lipophilicity. In addition, leads that already have such shortcomings can provide a difficult starting point for optimization4 ( ). Medicinal chemists can draw upon powerful new tools to plan how to systematically remove parts of the compound and determine the relative importance of the different components of the lead. Drawing on internal and external data, these tools enable researchers to pull apart and reassemble a molecule to reach its maximum potential. They can also help chemists quickly develop their synthesis plans and provide alternative pathways for synthesis of compounds of interest. BREAKING DOWN SILOS TO FOSTER COLLABORATION Pharmaceutical companies are working to break down the silos that are holding them back, ensuring proper integration of high-quality data and systems takes center stage. In lead optimization, it is important that team members across different disciplines have the ability to input data from their work in real time and access to broader data search and modeling capabilities. Moving this functionality to a single source and away from individual devices opens up the lines of communication, unlocking the value within the whole enterprise. Having systems in place to more effectively leverage internal and external data is a powerful tool in today’s evolving discovery environment, which leans more heavily on open innovation between pharmaceutical companies and research institutions, government entities, academia and even other pharmaceutical companies. The trend to this newer, more open model of collaboration is gaining steam and has the potential for companies that embrace this model to “access a large, diverse pool of ideas and experts which, in turn, could spur product innovation, speed time to market, reduce costs, and increase competitiveness.” (5) CONCLUSION Pharmaceutical companies are embracing these significant alterations to their operational model. This has brought a new appreciation that the success and efficiency of drug development centers on a company’s ability to identify its most robust leads and advance them through clinical development. This model requires pharmaceutical researchers with ready access to a combination of pharmacokinetic, efficacy, toxicity, safety and metabolic data and in silico profiling tools as they seek not only to fail drugs early, but promote their best lead compounds for further development.

REFERENCES 1. Geoui, T. 2016. Solving the Data Conundrum in Drug Discovery. Manufacturing Chemist Pharma, http://www.manufacturingchemist.com/news/article_page/ Solving_the_data_conundrum_in_drug_discovery/116129 2. Tufts Center for the Study of Drug Development. 2014. Cost to Develop and Win Marketing Approval for a New Drug Is $2.6 Billion, http://csdd.tufts.edu/news/complete_story/pr_tufts_csdd_2014_cost_study 3. Paul, S.M., Mytelka, D.S., Dunwiddie, C.T., Persinger, C.C., Munos, B.H., Lindborg, S.R. and Schacht, A.L. 2010. How to Improve R&D Productivity: the Pharmaceutical Industry’s Grand Challenge. Nature Review Drug Discovery, 9, 203–214. 4. Manly, C.J., Chandrasekhar, J., Ochterski, J.W., Hammer J.D. and Warfield, B.B. 2008. Strategies and Tactics for Optimizing the Hit-to-Lead Process and Beyond—A Computational Chemistry Perspective. Drug Discovery Today, 13(3/4), 99–109 5. Deloitte Center for Health Solutions. 2015. Executing an Open Innovation Model: Cooperation is Key to Competition for Biopharmaceutical Companies.

3 DISCOVER MORE To To see how Elsevier R&D Solutions for Pharma & Life Sciences can yield insights to improve drug development, visit https://www.elsevier.com/rd-solutions/pharma-and-life-sciences.

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April 2016