The BioPrint Approach

Krejsa, C.M., Horvath, D., Rogalski, S.L., Penzotti, J.E., Mao, B., Barbosa, F. and Migeon, J.C. (2003) Predicting ADME properties and side effects the BioPrint approach. Current Opinion in Drug Discovery and Development, 6, 471-480. 

Krejsa CM,Horvath D,Rogalski SL, etal. (2003) PredictingADMEproperties and side effects The BioPrint approach. Curr. Opin. Drug Discov. Devel. 6 470-480. 

Gozalbes R, Barbosa F, Froloff N, Horvath D. (2006) The BioPrint approach for the evaluation of ADME-T properties Application to the prediction of cytochrome P450 2D6 inhibition. In Physicochemical and Computational Strategies, pp. 395-415, VHCA, Zurich, Wiley-VCH, Weinheim. 

C. M. Krejsa, D. Horvath, S. L. Rogalski, J. E. Penzotti, B. Mao, E. Barbosa, J. C. Migeon (2003). Predicting ADME properties and side effects the BioPrint approach. Curr. Op. Drug Discov. Develop. 6 470-480. 

Krejsa CM, Horvath D, Rogalski SL, Penzotti JE, Mao B, Barbosa F, Migeon JC. Predicting ADME properties and side effects The BioPrint approach. Curr Opin Drug Discov Devel 2003 6 470-80. 

Figure 2.1 BioPrint approach the in vitro data are all measured in a consistent manner with full dose response for any activity >30% at 10pM the in vivo data are curated from available data, supplemented by custom measured data from collaborators.

In this chapter, the full BioPrint approach is described, as available from Cerep in terms of both the data set and the ability to have new compounds profiled and the results provided in the context of the BioPrint data set, including the known in vivo side effects of near neighbors in this biological space (see Section 2.5). The results for the differentiation of hit/lead compounds (see Section 2.3.2.1) sometimes use a subset of the 70-100 pharmacological assays that provide the maximum signal. Usually a decision on future work prioritization could be clearly made from the data from these subsets, saving time and money. For key reference/tool compounds, a full profile was used and is recommended to be used, as unexpected off-target activities may be found that cannot usually be predicted. 

The full matrix nature of the BioPrint database also enables an analysis of targets in drug chemical space. In this approach, a target is characterized by a fingerprint of the activities of a fixed set of compounds (the drug and reference compound set) against... 

BioPrint is particularly useful in placing the new drug candidates in the context of drugs and related compounds that together make up the history of medicinal chemistry. New candidates are run on the same assays as the BioPrint compounds and the resulting profile is analyzed. Profiles can be analyzed in two different ways each individual hit can be analyzed and assessed for potential ADR liabilities or the entire profile can be used to identify compounds with similar profiles. Potential ADR liabilities are assessed based on those of the similar compounds identified. These two different approaches will be discussed in the following sections. 

An early identification of the best leads is critical, and systematic biological profiling, as with Cerep BioPrint , enables this progress to a great extent. Experience with using this approach in a major pharmaceutical company (Pfizer) has confirmed this. One component of the issue can be stated that it is better to identify the best (not just potency) lead series than to try and find the best candidate from a possibly suboptimal series, which can happen at late stages of lead optimization, where it is very difficult to make major changes to the lead series chemistry. 

From the maao- to the microscale, the top-down approach directs the many features of relatively large scaffolds as is common with bioprinting techniques. Jakab et used a... 

---