Adaptive Manufacturing for Healthcare During the COVID-19 Emergency and Beyond.

During the COVID-19 pandemic, global health services have faced unprecedented demands. Many key workers in health and social care have experienced crippling shortages of personal protective equipment, and clinical engineers in hospitals have been severely stretched due to insufficient supplies of medical devices and equipment. Many engineers who normally work in other sectors have been redeployed to address the crisis, and they have rapidly improvised solutions to some of the challenges that emerged, using a combination of low-tech and cutting-edge methods. Much publicity has been given to efforts to design new ventilator systems and the production of 3D-printed face shields, but many other devices and systems have been developed or explored. This paper presents a description of efforts to reverse engineer or redesign critical parts, specifically a manifold for an anaesthesia station, a leak port, plasticware for COVID-19 testing, and a syringe pump lock box. The insights obtained from these projects were used to develop a product lifecycle management system based on Aras Innovator, which could with further work be deployed to facilitate future rapid response manufacturing of bespoke hardware for healthcare. The lessons learned could inform plans to exploit distributed manufacturing to secure back-up supply chains for future emergency situations. If applied generally, the concept of distributed manufacturing could give rise to "21st century cottage industries" or "nanofactories," where high-tech goods are produced locally in small batches.

A phase I trial of PRN1008, a novel reversible covalent inhibitor of Bruton's tyrosine kinase, in healthy volunteers.

AIM: To evaluate the safety, tolerability, and pharmacokinetics/pharmacodynamics of PRN1008, a novel Bruton's tyrosine kinase (BTK) inhibitor, in healthy volunteers, and thus determine the dose range for future clinical studies. METHODS: This was a two-part randomized, placebo controlled study in healthy volunteers using a liquid formulation. Part I was a single ascending dose design with dose levels of 50-1200 mg (n = 6 active, two placebos per cohort); Part II was a multiple ascending dose design, with dose regimens ranging from 300 to 900 mg daily, either four times or twice daily for 10 days. Plasma pharmacokinetics, adverse events, vital signs, electrocardiograms and laboratory parameters were assessed. BTK occupancy in peripheral blood mononuclear cells was evaluated as a marker of target engagement. RESULTS: PRN1008 was rapidly absorbed following oral administration, and was safe and well tolerated in all dose regimens evaluated in both single and multiple doses. PRN1008 demonstrated a large volume of distribution, and a half-life of approximately 3-4 h. BTK occupancy of >90% was observed within 4 h after dosing in both single and multiple dose regimens, and was closely linked to maximum plasma concentration. BTK occupancy decay was slow (-1.6% h-1 ), and occupancy was sustained despite drug concentrations being undetectable. No severe or serious adverse events occurred, and the most common adverse events were gastrointestinal in nature. CONCLUSIONS: PRN1008 was safe and well-tolerated following oral administration, and achieved high, sustained levels of BTK occupancy in peripheral blood mononuclear cells.

Hypoglycemia alarm enhancement using data fusion.

BACKGROUND: The acceptance of closed-loop blood glucose (BG) control using continuous glucose monitoring systems (CGMS) is likely to improve with enhanced performance of their integral hypoglycemia alarms. This article presents an in silico analysis (based on clinical data) of a modeled CGMS alarm system with trained thresholds on type 1 diabetes mellitus (T1DM) patients that is augmented by sensor fusion from a prototype hypoglycemia alarm system (HypoMon). This prototype alarm system is based on largely independent autonomic nervous system (ANS) response features. METHODS: Alarm performance was modeled using overnight BG profiles recorded previously on 98 T1DM volunteers. These data included the corresponding ANS response features detected by HypoMon (AiMedics Pty. Ltd.) systems. CGMS data and alarms were simulated by applying a probabilistic model to these overnight BG profiles. The probabilistic model developed used a mean response delay of 7.1 minutes, measurement error offsets on each sample of +/- standard deviation (SD) = 4.5 mg/dl (0.25 mmol/liter), and vertical shifts (calibration offsets) of +/- SD = 19.8 mg/dl (1.1 mmol/liter). Modeling produced 90 to 100 simulated measurements per patient. Alarm systems for all analyses were optimized on a training set of 46 patients and evaluated on the test set of 56 patients. The split between the sets was based on enrollment dates. Optimization was based on detection accuracy but not time to detection for these analyses. The contribution of this form of data fusion to hypoglycemia alarm performance was evaluated by comparing the performance of the trained CGMS and fused data algorithms on the test set under the same evaluation conditions. RESULTS: The simulated addition of HypoMon data produced an improvement in CGMS hypoglycemia alarm performance of 10% at equal specificity. Sensitivity improved from 87% (CGMS as stand-alone measurement) to 97% for the enhanced alarm system. Specificity was maintained constant at 85%. Positive predictive values on the test set improved from 61 to 66% with negative predictive values improving from 96 to 99%. These enhancements were stable within sensitivity analyses. Sensitivity analyses also suggested larger performance increases at lower CGMS alarm performance levels. CONCLUSION: Autonomic nervous system response features provide complementary information suitable for fusion with CGMS data to enhance nocturnal hypoglycemia alarms.