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Simulating the Drug Discovery Pipeline: a Monte Carlo Approach Melvin J Yu

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Simulating the Drug Discovery Pipeline: a Monte Carlo Approach Melvin J Yu View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Springer - Publisher Connector Yu Journal of Cheminformatics 2012, 4:32 http://www.jcheminf.com/content/4/1/32 RESEARCH ARTICLE Open Access Simulating the drug discovery pipeline: a Monte Carlo approach Melvin J Yu Abstract Background: The early drug discovery phase in pharmaceutical research and development marks the beginning of a long, complex and costly process of bringing a new molecular entity to market. As such, it plays a critical role in helping to maintain a robust downstream clinical development pipeline. Despite its importance, however, to our knowledge there are no published in silico models to simulate the progression of discrete virtual projects through a discovery milestone system. Results: Multiple variables were tested and their impact on productivity metrics examined. Simulations predict that there is an optimum number of scientists for a given drug discovery portfolio, beyond which output in the form of preclinical candidates per year will remain flat. The model further predicts that the frequency of compounds to successfully pass the candidate selection milestone as a function of time will be irregular, with projects entering preclinical development in clusters marked by periods of low apparent productivity. Conclusions: The model may be useful as a tool to facilitate analysis of historical growth and achievement over time, help gauge current working group progress against future performance expectations, and provide the basis for dialogue regarding working group best practices and resource deployment strategies. Background The study by Paul and co-workers1 indicates that the Over the past fifteen years, productivity of pharmaceut- lead optimization phase of drug discovery contributes ical research and development (R&D) in terms of significantly toward the out-of-pocket cost per launch of launched new molecular entities (NME) has at best been an NME. Yet, despite the importance of the discovery flat while costs have risen, resulting in a sharp decline in phase in pharmaceutical R&D, studies are lacking in the the number of first-in-class drugs entering the market as literature to provide guidance regarding the optimal dis- a percentage of R&D expenditures [1,2]. Adoption of tribution of scientific resources to generate a sufficient new technologies, six sigma initiatives [3,4] and adaptive quantity of preclinical candidate compounds at a rate clinical trial designs [5,6] have led to incremental that would lead to an NME entering the marketplace in improvements, but the productivity challenge facing the a specific time-frame. While particular sub-milestones pharmaceutical industry as a whole remains. Although and milestone names may vary between the major studies in this area largely focus on the clinical develop- pharmaceutical companies, the primary transition and ment aspect of the pipeline, a steady supply of quality go/no-go decision points for the early drug discovery preclinical drug candidates must be maintained at the pipeline can be categorized as those depicted in front end to prevent downstream gaps from forming. Figure 1. For example, shifting the focus from late stage phase III Attrition rates for compounds as they move through studies to early clinical proof of concept (POC) would the milestone system have been published1 and could be require an abundance of preclinical candidate com- used to approximate the average percent success rate of pounds that both add projected value to the company’s projects transitioning from one stage to the next. The portfolio and have a reasonable probability of technical question that we asked was whether this information success. could be employed to derive useful guidelines for human resource distribution by scientific discipline, and what Correspondence: [email protected] the optimal number of chemists and biologist might be Eisai Inc., 4 Corporate Dr., Andover, MA 01810, USA © 2012 Yu; licensee Chemistry Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Yu Journal of Cheminformatics 2012, 4:32 Page 2 of 14 http://www.jcheminf.com/content/4/1/32 values. Since these studies are usually, if not exclusively, performed on candidate compounds and not during the early phase of drug discovery, we assume for purposes of Figure 1 Diagram of major milestone transition and go/no-go the model that chemistry, biology and discovery DMPK decision points in the discovery phase of pharmaceutical R&D represent the rate limiting scientific disciplines during leading to preclinical development. Details as follows: (1) Conduct early discovery, depending on the scientific issues to be exploratory work that includes project conception, target validation addressed. Although data generated by the other specia- and screening. (2) Identify chemical lead(s) and establish target drug lized disciplines are certainly used by project teams to profile. (3) Optimize chemical properties and biological activity against the target drug profile. (4) Complete all necessary studies to help guide a structure-activity relationship (SAR) investi- allow an investigational new drug (IND) application to be filed. gation, the model assumes that there is enough resource support from the ancillary disciplines to allow decisions to be made within the discovery cycle times specified by to support a given portfolio of discovery project types the user. under a specific set of conditions. The algorithm takes as input a series of user-defined Although a number of simulation and game theory numbers that describes a typical project at the hit to lead algorithms have been used in clinical development mod- and lead optimization stages. This includes a target els, [7] to our knowledge there are no published in silico number of chemists and biologists, cycle time, milestone models that simulate the progression of projects through transition probabilities, and project type. Project type in- early discovery, i.e. from conceptualization to candidate clude those that are biology-driven (i.e., projects that selection. Given the importance of the discovery phase typically involve high-throughput screening (HTS) to pharmaceutical R&D, we developed a Monte Carlo against a biological target of interest), and those that are simulation algorithm for modeling discrete virtual pro- chemistry-driven with a clearly identified chemical lead, jects as they move through the discovery milestone sys- but not necessarily information about mechanism of ac- tem ending with entry into preclinical development. tion (MOA). In either case, the link between MOA and Each virtual project has associated with it a dynamic human disease may or may not be established. Another number of chemists and biologists with drug metabol- type of project are those that are “follow-on” or “back- ism/pharmacokinetics (DMPK) support that varies up” projects that share biological resources and thera- according to project priority, type, and milestone stage. peutic target with another on-going discovery program, Such an in silico model would allow multiple variables albeit with a different structural scaffold or chemical to be evaluated for a given portfolio of drug discovery lead. In such cases, the SAR will likely diverge from its programs in the context of expected productivity related project, and will, if successful, afford a candidate metrics. compound with a similar drug profile, but with a differ- ent scaffold and therefore a different set of physico- Methods chemical properties. The algorithm treats the two as With scientific specialization comes the need for cross- separate and distinct projects, albeit with different levels functional teams [8]. Pharmaceutical R&D is no excep- of chemical and biological support, whose outcome tion. For purposes of the model, however, a simplifying could both lead to preclinical candidate compounds. assumption is made that drug discovery project teams The user has the ability to specify the percent of projects consist of medicinal/synthetic chemists, biologists cap- that are “follow-on” and the percent of those that are able of developing and running multi-tiered in vitro chemistry versus biology driven (Figure 2). assay systems and relevant in vivo animal models as well Since project cycle time is dependent on the level of as representation by members of the DMPK group. resource support, a simple monotonic function was used While scientists from a number of specialized scientific to correct the time to milestone decision. For example, disciplines (e.g. process chemistry research, analytical the algorithm may allow a project at the hit to lead stage chemistry, computational chemistry, biopharmaceutical to reach lead optimization in one year if fully staffed, but assessment, formulation, in vivo imaging, etc.) partici- would correct that value to two years if only half-staffed. pate to varying degrees in the drug discovery process, If additional resources become available in-between the their roles are assumed not to be rate limiting with re- hit to lead and lead optimization stages, then the time to gard to decision-making until projects approach the pre- milestone decision would be adjusted according to clinical development go/no-go decision point. Cost and Equation 1. In this fashion, project
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