The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the number of efficacy- and safety-related failures by analysing possible links to the physicochemical properties of small-molecule drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality and indicates for the first time a link between the physicochemical properties of compounds and clinical failure due to safety issues. The results also suggest that further control of physicochemical properties is unlikely to have a significant effect on attrition rates and that additional work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.

Venturing into the immensity of the small molecule universe to identify novel chemical structure is a much discussed objective of many methods proposed by the chemoinformatics community. To this end, numerous approaches using techniques from the fields of computational de novo design, virtual screening and reaction informatics, among others, have been proposed. Although in principle this objective is commendable, in practice there are several obstacles to useful exploitation of the chemical space. Prime among them are the sheer number of theoretically feasible compounds and the practical concern regarding the synthesizability of the chemical structures conceived using in silico methods. We present the Proximal Lilly Collection initiative implemented at Eli Lilly and Co. with the aims to (i) define the chemical space of small, drug-like compounds that could be synthesized using in-house resources and (ii) facilitate access to compounds in this large space for the purposes of ongoing drug discovery efforts. The implementation of PLC relies on coupling access to available synthetic knowledge and resources with chemo/reaction informatics techniques and tools developed for this purpose. We describe in detail the computational framework supporting this initiative and elaborate on the characteristics of the PLC virtual collection of compounds. As an example of the opportunities provided to drug discovery researchers by easy access to a large, realistically feasible virtual collection such as the PLC, we describe a recent application of the technology that led to the discovery of selective kinase inhibitors.

Currently published studies have implicated that microRNAs (miRNAs) including exosomes-encapsulated miRNAs play a critical role in rheumatoid arthritis (RA). Previously, we have found that exosomes-encapsulated miR-548a-3p was significantly decreased in serum samples from RA patients by miRNAs microarray analysis. However, little is known of the role of miR-548a-3p in the development and progression of RA. In this study, we aim to investigate the underlying molecular mechanisms of miR-548a-3p in RA, which will provide new insight into understanding the pathogenesis of RA and identifying novel therapeutics targets for this disease. As validated by quantitative real-time polymerase chain reaction (qRT-PCR), the expression of miR-548a-3p in serum exosomes and peripheral blood mononuclear cells (PBMCs) of RA patients (n = 76) was obviously down-regulated compared with healthy controls (n = 20). Serum exosomal miR-548a-3p was negatively associated with levels of CRP, RF, and ESR in serum of patients with RA. MiR-548a-3p could inhibit the proliferation and activation of pTHP-1 cells by regulating the TLR4/NF-κB signaling pathway. Accordingly, exosomes-delivered miR-548a-3p may be a critical factor predicting the disease activity of RA. MiR-548a-3p/TLR4/NF-κB axis can serve as promising targets for RA diagnosis and treatment.

The need for synthetic route design arises frequently in discovery-oriented chemistry organizations. While traditionally finding solutions to this problem has been the domain of human experts, several computational approaches, aided by the algorithmic advances and the availability of large reaction collections, have recently been reported. Herein we present our own implementation of a retrosynthetic analysis method and demonstrate its capabilities in an attempt to identify synthetic routes for a collection of approved drugs. Our results indicate that the method, leveraging on reaction transformation rules learned from a large patent reaction dataset, can identify multiple theoretically feasible synthetic routes and, thus, support research chemist everyday efforts. Electronic supplementary material The online version of this article (10.1186/s13321-018-0323-6) contains supplementary material, which is available to authorized users.

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