BIOAVAILABILITY STUDY OF ONDANSETRON GEL IN RABBITS AND HUMAN VOLUNTEERS APPLING UPLC AS ANALYTICAL TOOL AND EVALUATION OF THE ANTIEMETIC EFFECT OF ONDANSETRON GEL IN CISPLATIN-INDUCED EMESIS IN RATS

Objective: This study was undertaken to determine the bioavailability of ondansetron gel in experimental animals and humans applying UPLC as an analytical tool and evaluation of the antiemetic effect of ondansetron gel in cisplatin-induced emesis in rats. Methods: Ondansetron gel (F13: sodium alginate 7% w/w) was used, marketed I. V. ondansetron (Zofran) ® was chosen as reference. The bioavailability study in rabbits was selected as a parallel design using nine healthy rabbits divided into three groups whereas, bioavailability study in humans was an open-label, wherein 6 healthy subjects administered ondansetron gel. The potential effect of ondansetron gel was evaluated for the prevention of different phases of emesis motivated by exposure to antineoplastic drugs (cisplatin) by determination of body weight loss, water and food intake applying kaolin-pica model in rats using seventy-two rats divided into six groups. Results: Ondansetron gel (0.5%) showed detectable plasma concentration 22.833±2.17 ng/m1 after ¼ h and 419.55±2.17 ng/ml after 1-h post-treatment in rabbits and human respectively and concentration was maintained above-reported minimum effective concentration for more than 2.5 h for rabbits and 7 h for humans compared to 1.75 h after I. V. administration. The ondansetron gel significantly reduces all phases of cisplatin-induced emesis and a decrease in body weight, water, and food consumption was significantly attenuated. Conclusion: Based on the high efficacy of gel on emesis induced by cisplatin, and its high bioavailability, transdermal ondansetron gel could be a promising convenient system to prevent nausea and vomiting following administration of antineoplastic drugs

Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma

Multiple myeloma, a plasma cell malignancy, is the second most common blood cancer. Despite extensive research, disease heterogeneity is poorly characterized, hampering efforts for early diagnosis and improved treatments. Here, we apply single cell RNA sequencing to study the heterogeneity of 40 individuals along the multiple myeloma progression spectrum, including 11 healthy controls, demonstrating high interindividual variability that can be explained by expression of known multiple myeloma drivers and additional putative factors. We identify extensive subclonal structures for 10 of 29 individuals with multiple myeloma. In asymptomatic individuals with early disease and in those with minimal residual disease post-treatment, we detect rare tumor plasma cells with molecular characteristics similar to those of active myeloma, with possible implications for personalized therapies. Single cell analysis of rare circulating tumor cells allows for accurate liquid biopsy and detection of malignant plasma cells, which reflect bone marrow disease. Our work establishes single cell RNA sequencing for dissecting blood malignancies and devising detailed molecular characterization of tumor cells in symptomatic and asymptomatic patients

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery