Lead optimization property profile

Van de Waterbeemd, H. Property-based lead optimization. In Biological and Physicochemical Profiling in Drug Research, Testa, B., Kramer, S. D., Wunderli-Allenspach, H., Folkers, G. (eds.), VHCA, Zurich and Wiley-VCH, Weinheim, 2006, pp. 25-45. 

Lead refinement where sufficient information has been accumulated to use (Q)SAR-based design of compounds to achieve higher (optimal) efficacy and/or better property profiles. 

The principal issue in the drug discovery process is the high failure rate in the clinical trials, mainly due to liabilities related to poor pharmacokinetics (PK), poor efficacy, and high toxicity. The earlier lead optimization (LO) phase then represents a crucial step in the drug discovery process, since it involves the preparation and the selection of suitable drug candidates. In view of the increasing need for speed in the preclinical research and development, the determination of activity and selectivity is performed simultaneously with the evaluation of pharmacokinetic and toxicity properties. This multiparametric approach allows the early selection of the compounds with the best overall balanced druglike profile [1]. 

Also at this stage, pharmaceutical profiling assays, such as solubility, permeability, and metabolic stability provide a multivariate property profile of HTS hits that contribute to the team s holistic selection of leads for synthetic optimization. The pharmaceutical-property profile is considered in relation to the ensemble of activity properties for making informed decisions. Conversely, an alert to poor properties can save the team effort and time on problematic structures, or forewarn of issues that must later be solved via synthetic modification, salt form, or formulation. 

The lead optimization process is highly iterative, multiparametric, and intensely interdisciplinary. Both qualitative and quantitative information is routinely obtained on analog series of compounds to correlate changes in chemical structure to biological and pharmacological data. The resulting quantitative information provides the basis for the comparison of lead compounds and the ability to establish structure activity relationships (SAR). This multistep evaluation and optimization of ADME-PK properties continues until a defined drug profile is achieved. 

Knowing that we needed to develop a CNS drug, we then reduced the number of rotatable bonds of compound 11 and designed the rigid, compact 5,5 -diphenyl iminohydantoin core structure (compound 12) which, despite being only weakly active, now showed excellent lead-like properties with good LE, much better LLE, and an overall favorable profile with respect to cellular potency, selectivity, rat PK, and brain penetration (Fig. 4e). Thus, compound 12 was the superior choice for lead optimization. 

When working on a specific dmg discovery project, medicinal chemists are guided by property measurements and experience to achieve a desired dmg property profile. Since not all properties can be measured during lead optimization (human PK and toxicity, for example), even compounds that fit the desired property profile often fail in the development stages. It is this uncertainty which drives our efforts to understand what stmctural features or molecular properties constitute the liabilities that might cause candidates to fail. For this reason, dmg-likeness is most commonly thought of in terms of those liabilities, and it is assessed to alert chemists about them. 

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