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Monoterpene indole alkaloids (MIAs) are a diverse family of complex plant secondary metabolites with many medicinal properties, including the essential anti-cancer therapeutics vinblastine and vincristine1. As MIAs are difficult to chemically synthesize, the world’s supply chain for vinblastine relies on low-yielding extraction and purification of the precursors vindoline and catharanthine from the plant Catharanthus roseus, which is then followed by simple in vitro chemical coupling and reduction to form vinblastine at an industrial scale2,3. Here, we demonstrate the de novo microbial biosynthesis of vindoline and catharanthine using a highly engineered yeast, and in vitro chemical coupling to vinblastine. The study showcases a very long biosynthetic pathway refactored into a microbial cell factory, including 30 enzymatic steps beyond the yeast native metabolites geranyl pyrophosphate and tryptophan to catharanthine and vindoline. In total, 56 genetic edits were performed, including expression of 34 heterologous genes from plants, as well as deletions, knock-downs and overexpression of ten yeast genes to improve precursor supplies towards de novo production of catharanthine and vindoline, from which semisynthesis to vinblastine occurs. As the vinblastine pathway is one of the longest MIA biosynthetic pathways, this study positions yeast as a scalable platform to produce more than 3,000 natural MIAs and a virtually infinite number of new-to-nature analogues.

Research on and commercial development of the artificial pancreas (AP) continue to progress rapidly, and the AP promises to become a part of clinical care. In this report, members of the JDRF Artificial Pancreas Project Consortium in collaboration with the wider AP community 1) advocate for the use of continuous glucose monitoring glucose metrics as outcome measures in AP trials, in addition to HbA1c, and 2) identify a short set of basic, easily interpreted outcome measures to be reported in AP studies whenever feasible. Consensus on a broader range of measures remains challenging; therefore, reporting of additional metrics is encouraged as appropriate for individual AP studies or study groups. Greater consistency in reporting of basic outcome measures may facilitate the interpretation of study results by investigators, regulatory bodies, health care providers, payers, and patients themselves, thereby accelerating the widespread adoption of AP technology to improve the lives of people with type 1 diabetes.

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OBJECTIVEWe evaluated the safety and efficacy of closed-loop basal insulin delivery during sleep and after regular meals and unannounced periods of exercise.RESEARCH DESIGN AND METHODSTwelve adolescents with type 1 diabetes (five males; mean age 15.0 [SD 1.4] years; HbA1c 7.9 [0.7]%; BMI 21.4 [2.6] kg/m2) were studied at a clinical research facility on two occasions and received, in random order, either closed-loop basal insulin delivery or conventional pump therapy for 36 h. During closed-loop insulin delivery, pump basal rates were adjusted every 15 min according to a model predictive control algorithm informed by subcutaneous sensor glucose levels. During control visits, subjects’ standard infusion rates were applied. Prandial insulin boluses were given before main meals (50–80 g carbohydrates) but not before snacks (15–30 g carbohydrates). Subjects undertook moderate-intensity exercise, not announced to the algorithm, on a stationary bicycle at a 140 bpm heart rate in the morning (40 min) and afternoon (20 min). Primary outcome was time when plasma glucose was in the target range (71–180 mg/dL).RESULTSClosed-loop basal insulin delivery increased percentage time when glucose was in the target range (median 84% [interquartile range 78–88%] vs. 49% [26–79%], P = 0.02) and reduced mean plasma glucose levels (128 [19] vs. 165 [55] mg/dL, P = 0.02). Plasma glucose levels were in the target range 100% of the time on 17 of 24 nights during closed-loop insulin delivery. Hypoglycemia occurred on 10 occasions during control visits and 9 occasions during closed-loop delivery (5 episodes were exercise related, and 4 occurred within 2.5 h of prandial bolus).CONCLUSIONSDay-and-night closed-loop basal insulin delivery can improve glucose control in adolescents. However, unannounced moderate-intensity exercise and excessive prandial boluses pose challenges to hypoglycemia-free closed-loop basal insulin delivery.

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OBJECTIVEThis study evaluated closed-loop insulin delivery with a model predictive control (MPC) algorithm during early (12–16 weeks) and late gestation (28–32 weeks) in pregnant women with type 1 diabetes.RESEARCH DESIGN AND METHODSTen women with type 1 diabetes (age 31 years, diabetes duration 19 years, BMI 24.1 kg/m2, booking A1C 6.9%) were studied over 24 h during early (14.8 weeks) and late pregnancy (28.0 weeks). A nurse adjusted the basal insulin infusion rate from continuous glucose measurements (CGM), fed into the MPC algorithm every 15 min. Mean glucose and time spent in target (63–140 mg/dL), hyperglycemic (>140 to ≥180 mg/dL), and hypoglycemic (<63 to ≤50 mg/dL) were calculated using plasma and sensor glucose measurements. Linear mixed-effects models were used to compare glucose control during early and late gestation.RESULTSDuring closed-loop insulin delivery, median (interquartile range) plasma glucose levels were 117 (100.8–154.8) mg/dL in early and 126 (109.8–140.4) mg/dL in late gestation (P = 0.72). The overnight mean (interquartile range) plasma glucose time in target was 84% (50–100%) in early and 100% (94–100%) in late pregnancy (P = 0.09). Overnight mean (interquartile range) time spent hyperglycemic (>140 mg/dL) was 7% (0–40%) in early and 0% (0–6%) in late pregnancy (P = 0.25) and hypoglycemic (<63 mg/dL) was 0% (0–3%) and 0% (0–0%), respectively (P = 0.18). Postprandial glucose control, glucose variability, insulin infusion rates, and CGM sensor accuracy were no different in early or late pregnancy.CONCLUSIONSMPC algorithm performance was maintained throughout pregnancy, suggesting that overnight closed-loop insulin delivery could be used safely during pregnancy. More work is needed to achieve optimal postprandial glucose control.