Predictors of humoral response to SARS-CoV-2 mRNA vaccine BNT162b2 in patients receiving maintenance dialysis.

Objective: Patients on dialysis are at high risk for severe COVID-19 and associated morbidity and mortality. We examined the humoral response to SARS-CoV-2 mRNA vaccine BNT162b2 in a maintenance dialysis population. Design: Single-center cohort study. Setting and participants: Adult maintenance dialysis patients at 3 outpatient dialysis units of a large academic center. Methods: Participants were vaccinated with 2 doses of BNT162b2, 3 weeks apart. We assessed anti-SARS-CoV-2 spike antibodies (anti-S) ∼4-7 weeks after the second dose and evaluated risk factors associated with insufficient response. Definitions of antibody response are as follows: nonresponse (anti-S level, <50 AU/mL), low response (anti-S level, 50-839 AU/mL), and sufficient response (anti-S level, ≥840 AU/mL). Results: Among the 173 participants who received 2 vaccine doses, the median age was 60 years (range, 28-88), 53.2% were men, 85% were of Black race, 86% were on in-center hemodialysis and 14% were on peritoneal dialysis. Also, 7 participants (4%) had no response, 27 (15.6%) had a low response, and 139 (80.3%) had a sufficient antibody response. In multivariable analysis, factors significantly associated with insufficient antibody response included end-stage renal disease comorbidity index score ≥5 and absence of prior hepatitis B vaccination response. Conclusions: Although most of our study participants seroconverted after 2 doses of BNT162b2, 20% of our cohort did not achieve sufficient humoral response. Our findings demonstrate the urgent need for a more effective vaccine strategy in this high-risk patient population and highlight the importance of ongoing preventative measures until protective immunity is achieved.

'Black Box' to 'Conversational' Machine Learning: Ondansetron Reduces Risk of Hospital-Acquired Venous Thromboembolism.

Machine learning, combined with a proliferation of electronic healthcare records (EHR), has the potential to transform medicine by identifying previously unknown interventions that reduce the risk of adverse outcomes. To realize this potential, machine learning must leave the conceptual 'black box' in complex domains to overcome several pitfalls, like the presence of confounding variables. These variables predict outcomes but are not causal, often yielding uninformative models. In this work, we envision a 'conversational' approach to design machine learning models, which couple modeling decisions to domain expertise. We demonstrate this approach via a retrospective cohort study to identify factors which affect the risk of hospital-acquired venous thromboembolism (HA-VTE). Using logistic regression for modeling, we have identified drugs that reduce the risk of HA-VTE. Our analysis reveals that ondansetron, an anti-nausea and anti-emetic medication, commonly used in treating side-effects of chemotherapy and post-general anesthesia period, substantially reduces the risk of HA-VTE when compared to aspirin (11% vs. 15% relative risk reduction or RRR, respectively). The low cost and low morbidity of ondansetron may justify further inquiry into its use as a preventative agent for HA-VTE. This case study highlights the importance of engaging domain expertise while applying machine learning in complex domains.

Open Source Drug Discovery with the Malaria Box Compound Collection for Neglected Diseases and Beyond.

A major cause of the paucity of new starting points for drug discovery is the lack of interaction between academia and industry. Much of the global resource in biology is present in universities, whereas the focus of medicinal chemistry is still largely within industry. Open source drug discovery, with sharing of information, is clearly a first step towards overcoming this gap. But the interface could especially be bridged through a scale-up of open sharing of physical compounds, which would accelerate the finding of new starting points for drug discovery. The Medicines for Malaria Venture Malaria Box is a collection of over 400 compounds representing families of structures identified in phenotypic screens of pharmaceutical and academic libraries against the Plasmodium falciparum malaria parasite. The set has now been distributed to almost 200 research groups globally in the last two years, with the only stipulation that information from the screens is deposited in the public domain. This paper reports for the first time on 236 screens that have been carried out against the Malaria Box and compares these results with 55 assays that were previously published, in a format that allows a meta-analysis of the combined dataset. The combined biochemical and cellular assays presented here suggest mechanisms of action for 135 (34%) of the compounds active in killing multiple life-cycle stages of the malaria parasite, including asexual blood, liver, gametocyte, gametes and insect ookinete stages. In addition, many compounds demonstrated activity against other pathogens, showing hits in assays with 16 protozoa, 7 helminths, 9 bacterial and mycobacterial species, the dengue fever mosquito vector, and the NCI60 human cancer cell line panel of 60 human tumor cell lines. Toxicological, pharmacokinetic and metabolic properties were collected on all the compounds, assisting in the selection of the most promising candidates for murine proof-of-concept experiments and medicinal chemistry programs. The data for all of these assays are presented and analyzed to show how outstanding leads for many indications can be selected. These results reveal the immense potential for translating the dispersed expertise in biological assays involving human pathogens into drug discovery starting points, by providing open access to new families of molecules, and emphasize how a small additional investment made to help acquire and distribute compounds, and sharing the data, can catalyze drug discovery for dozens of different indications. Another lesson is that when multiple screens from different groups are run on the same library, results can be integrated quickly to select the most valuable starting points for subsequent medicinal chemistry efforts.