Compounds from the Medicines for Malaria Venture Box Inhibit In Vitro Growth of Babesia divergens, a Blood-Borne Parasite of Veterinary and Zoonotic Importance

Babesiosis is an infectious disease with an empty drug pipeline. A search inside chemical libraries for novel potent antibabesial candidates may help fill such an empty drug pipeline. A total of 400 compounds (200 drug-like and 200 probe-like) from the Malaria Box were evaluated in the current study against the in vitro growth of Babesia divergens (B. divergens), a parasite of veterinary and zoonotic importance. Novel and more effective anti-B. divergens drugs than the traditionally used ones were identified. Seven compounds (four drug-like and three probe-like) revealed a highly inhibitory effect against the in vitro growth of B. divergens, with IC50s ≤ 10 nanomolar. Among these hits, MMV006913 exhibited an IC50 value of 1 nM IC50 and the highest selectivity index of 32,000. The atom pair fingerprint (APfp) analysis revealed that MMV006913 and MMV019124 showed maximum structural similarity (MSS) with atovaquone and diminazene aceturate (DA), and with DA and imidocarb dipropionate (ID), respectively. MMV665807 and MMV665850 showed MMS with each other and with ID. Of note, a high concentration (0.75 IC50) of MMV006913 caused additive inhibition of B. divergens growth when combined with DA at 0.75 or 0.50 IC50. The Medicines for Malaria Venture box is a treasure trove of anti-B. divergens candidates according to the obtained results.

Cyclohexyl-griselimycin is active against Mycobacterium abscessus in mice

Cyclohexyl-griselimycin is a preclinical candidate for tuberculosis (TB). Here, we show that this oral cyclodepsipeptide is also active against the intrinsically drug resistant non-tuberculous mycobacterium Mycobacterium abscessus in vitro and in a mouse model of infection. This adds a novel advanced lead compound to the M. abscessus drug pipeline and supports a strategy of screening chemical matter generated in TB drug discovery efforts to fast track the discovery of novel antibiotics against M. abscessus .

Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

Multilayer network analysis of the drugs development cycle in the global pharmaceutical industry

Drug development is a time-consuming process from the start of research to obtaining approval, and the probability of success with a candidate compound is extremely low. We aim to understand the characteristics of the flow and localization of knowledge during drug development in the global pharmaceutical industry. We analyze the multilayer network constructed with the drug pipeline layer, global supply chain layer, and global ownership layer. First, we identify the bow-tie structure and the community structure of each network layer. The obtained bow-tie structure shows the large strongly connected component and suggests that the knowledge flow in drug pipelines has similar characteristics as the supply chain network. The communities in each layer are characterized by country, category of the company, and bow tie component. We then study the multilayer network’s knowledge flow, conduct a statistical test, and verify the significance of the overlapping links between the drug pipeline and supply chain layers. Our results suggest a strong connection between open innovation in the pharmaceutical industry and firms’ economic activities in the supply chain.