Daniel Cravo

Structure-based design of 7-azaindole-pyrrolidine amides as inhibitors of 11β-hydroxysteroid dehydrogenase type I

Indole-pyrrolidines were identified as inhibitors of 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) by high-throughput screening. Optimisation of the initial hit through structure-based design led to 7-azaindole-derivatives, with the best analogues displaying single digit nanomolar IC(50) potency. The modeling hypotheses were confirmed by solving the X-ray co-crystal structure of one of the lead compounds. These compounds were selective against 11β-hydroxysteroid dehydrogenase type 2 (selectivity ratio >200) and exhibited good inhibition of 11β-HSD1 (IC(50)<1μM) in a cellular model (3T3L1 adipocytes).

Time-Trajectories in Efficiency Maps as Effective Guides for Drug Discovery Efforts

Modern preclinical drug discovery programs generate vast amounts of data from which medicinal chemists try to distinguish emerging trends, in order to identify and optimize compounds that ultimately will become drugs. We propose a direct and intuitive two-dimensional graphical representation that results in maps based on the combined use of two complementary ligand efficiency indices (LEIs): efficiency per size and efficiency per polarity. We introduce the notion of ‘time-trajectory’ in these maps and suggest that they are an effective way to monitor the progress of chemical series, therefore complementing the conventional SAR tables. A majority of modern drug discovery programs aim at identifying compounds that modulate the activity of a chosen target. This is commonly achieved through High Throughput Screening (HTS) of large compounds collections, the outcome of which being a list of compounds flagged as actives and inactives, plus the corresponding activity values. Active compounds can also have other diverse origins (i.e. , inhibitors from literature, substrate analogs, etc...). The relationships between the compounds structures and their activities (Structure-Activity Relationships, SAR) are used in medicinal chemistry programs to optimize the initial hits towards to optimum preclinical candidates. The large volume of information generated in an HTS run needs to be processed, and the first step is usually to group compounds into chemical series, defined by similar or closely related scaffolds. To effectively rank and prioritize the perceived series, several parameters need to be taken into account, including amongst others: overall potency given as Ki or IC50 values in the corresponding assays; proportion of actives/inactives in the series; potential for further chemical modifications; physicochemical properties (solubility, stability, etc.) ; in silico predicted properties (toxicity, off-target activities, etc.) ; freedom to operate with regards to intellectual property. Key iconic elements in any drug discovery effort are the SAR tables, where the different chemical modifications to a series are listed, relating the scaffolds substituent (R groups) to the biological activities of the resulting chemical entities. In the past, numerical potency has been the parameter dominating the optimization process. Identifying optimal objective descriptors to effectively guide drug discovery continues to be an unsolved problem in medicinal chemistry. In the last few years, the concept of ‘Ligand Efficiency’ (LE) in relation to their affinity towards the corresponding target has become more accepted and used by the practicing medicinal chemist. This is true, particularly, in regard to the size of the ligand. Recently, the combined use of two complementary Ligand Efficiency Indices (LEIs) has been proposed to map chemical entities within the context of the targets for which they have biological activity; this has been referred to as AtlasCBS. In this communication, we summarize the definitions of the most relevant LEIs, present the initial and the most recent two-dimensional maps in the LEIs space (also referred to as efficiency maps), and introduce a representation of the drug discovery effort as a ‘time-trajectory’ in these maps. We suggest this representation as a means to fruitfully complement the traditional SAR tables by permitting to follow the design sequence of the drug discovery process. To that aim, three previously published chemical series of 11b-hydroxysteroid dehydrogenase type I (11b-HSD1) inhibitors will illustrate the different uses of LEIs and introduce the proposed concept of ‘time trajectories’ in the LEIs space. These series were based on pentanedioic acid diamine spirocarboxamide and azaindole scaffolds. While only the BEI was originally used to monitor the progress of these three series we exemplify here two categories of LEIs : one related to size (Binding Efficiency Index, BEI and related) and one related to polarity (Surface Efficiency