Can metformin prevent cancer relative to sulfonylureas? A target trial emulation accounting for competing risks and poor overlap via double/debiased machine learning estimators.

There is mounting interest in the possibility that metformin, indicated for glycemic control in type 2 diabetes, has a range of additional beneficial effects. Randomized trials have shown that metformin prevents adverse cardiovascular events, and metformin use has also been associated with reduced cognitive decline and cancer incidence. In this paper, we dig more deeply into whether metformin prevents cancer by emulating target randomized trials comparing metformin to sulfonylureas as first line diabetes therapy using data from Clinical Practice Research Datalink, a U.K. primary care database (1987-2018). We included individuals with diabetes, no prior cancer diagnosis, no chronic kidney disease, and no prior diabetes therapy who initiated metformin (N=93353) or a sulfonylurea (N=13864). In our cohort, the estimated overlap weighted additive separable direct effect of metformin compared to sulfonylureas on cancer risk at 6 years was -1% (.95 CI=-2.2%, 0.1%), which is consistent with metformin providing no direct protection against cancer incidence or substantial protection. The analysis faced two methodological challenges-poor overlap, and pre-cancer death as a competing risk. To address these issues while minimizing nuisance model misspecification, we develop and apply double/debiased machine learning estimators of overlap weighted separable effects in addition to more traditional effect estimates.

Systematically exploring repurposing effects of antihypertensives.

With availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on nonindicated outcomes) is feasible. In this article, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of antihypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of antihypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of postprocessing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context.

Foundational model aided automatic high-throughput drug screening using self-controlled cohort study.

BACKGROUND: Developing medicine from scratch to governmental authorization and detecting adverse drug reactions (ADR) have barely been economical, expeditious, and risk-averse investments. The availability of large-scale observational healthcare databases and the popularity of large language models offer an unparalleled opportunity to enable automatic high-throughput drug screening for both repurposing and pharmacovigilance. OBJECTIVES: To demonstrate a general workflow for automatic high-throughput drug screening with the following advantages: (i) the association of various exposure on diseases can be estimated; (ii) both repurposing and pharmacovigilance are integrated; (iii) accurate exposure length for each prescription is parsed from clinical texts; (iv) intrinsic relationship between drugs and diseases are removed jointly by bioinformatic mapping and large language model - ChatGPT; (v) causal-wise interpretations for incidence rate contrasts are provided. METHODS: Using a self-controlled cohort study design where subjects serve as their own control group, we tested the intention-to-treat association between medications on the incidence of diseases. Exposure length for each prescription is determined by parsing common dosages in English free text into a structured format. Exposure period starts from initial prescription to treatment discontinuation. A same exposure length preceding initial treatment is the control period. Clinical outcomes and categories are identified using existing phenotyping algorithms. Incident rate ratios (IRR) are tested using uniformly most powerful (UMP) unbiased tests. RESULTS: We assessed 3,444 medications on 276 diseases on 6,613,198 patients from the Clinical Practice Research Datalink (CPRD), an UK primary care electronic health records (EHR) spanning from 1987 to 2018. Due to the built-in selection bias of self-controlled cohort studies, ingredients-disease pairs confounded by deterministic medical relationships are removed by existing map from RxNorm and nonexistent maps by calling ChatGPT. A total of 16,901 drug-disease pairs reveals significant risk reduction, which can be considered as candidates for repurposing, while a total of 11,089 pairs showed significant risk increase, where drug safety might be of a concern instead. CONCLUSIONS: This work developed a data-driven, nonparametric, hypothesis generating, and automatic high-throughput workflow, which reveals the potential of natural language processing in pharmacoepidemiology. We demonstrate the paradigm to a large observational health dataset to help discover potential novel therapies and adverse drug effects. The framework of this study can be extended to other observational medical databases.

Low auto-fluorescence fabrication methods for plastic nanoslits.

Plastic nanofluidic devices are becoming increasingly important for biological and chemical applications. However, they suffer from high auto-fluorescence when used for on-chip optical detection. In this study, the auto-fluorescence problem of plastic nanofluidic devices was remedied by newly developed fabrication methods that minimise their auto-fluorescence: one by depositing a gold (Au) layer on them, the other by making them ultra-thin. In the first method, the Au layer [minimum thickness is 40 nm on 150 µm SU-8, 50 nm on 1 mm polyethylene terephthalate (PET), and 40 on 2 nm polymethyl methacrylate (PMMA)] blocks the auto-fluorescence of the polymer; in the second method, auto-fluorescence is minimised by making the chips ultra-thin, selected operating thickness of SU-8 is 20 µm, for PET it is 150 µm, and for PMMA it is 0.8 mm.

Fabrication of PMMA nanofluidic electrochemical chips with integrated microelectrodes.

A novel method based on plasma etching was proposed for monolithically integrating planar nanochannels and microelectrodes on a poly (methyl methacrylate) (PMMA) plate, and complete PMMA nanofluidic electrochemical chips with integrated microelectrodes were constructed by bonding with another PMMA plate containing microchannels. The fabrication sequences of nanochannels and microelectrodes were optimized. The oxygen plasma etching rate of PMMA nanochannels was studied, and the average rate was 15 nm/min under optimal conditions. An UV-ozone assisted thermal bonding method was developed to realize a low-temperature chip bonding, and the variations in width and depth of nanochannels before and after bonding were 2% and 5%, respectively. As a demonstration, a nanoparticle crystal (NPC)-based nanofluidic biosensor with integrated Ag microelectrodes was designed and fabricated. Sub-microchannel arrays with a depth of 400 nm and a width of 30 µm on the biosensor functioned as filters, and trapped 540 nm silica nanoparticles modified with streptavidin inside the connected microchannel to assemble the NPC. The interspaces in the NPC formed a three-dimensional nanochannel network with an equivalent diameter of 81 nm. By measuring the conductance across the NPC, a high quality nanofluidic sensing of biotin was achieved. The limit of detection was 1 aM, and the detection range was from 1 aM to 0.1 nM.

Fabrication of two dimensional polyethylene terephthalate nanofluidic chip using hot embossing and thermal bonding technique.

We present in this paper a method for obtaining a low cost and high replication precision 2D (two dimensional) nanofluidic chip with a PET (polyethylene terephthalate) sheet, which uses hot embossing and a thermal bonding technique. The hot embossing process parameters were optimized by both experiments and the finite element method to improve the replication precision of the 2D nanochannels. With the optimized process parameters, 174.67 ± 4.51 nm wide and 179.00 ± 4.00 nm deep nanochannels were successfully replicated into the PET sheet with high replication precision of 98.4%. O2 plasma treatment was carried out before the bonding process to decrease the dimension loss and improve the bonding strength of the 2D nanofluidic chip. The bonding parameters were optimized by bonding rate of the nanofluidic chip. The experiment results show that the bonding strength of the 2D PET nanofluidic chip is 0.664 MPa, and the total dimension loss of 2D nanochannels is 4.34 ± 7.03 nm and 18.33 ± 9.52 nm, in width and depth, respectively. The fluorescence images demonstrate that there is no blocking or leakage over the entire micro- and nanochannels. With this fabrication technology, low cost polymer nanochannels can be fabricated, which allows for commercial manufacturing of nano-components.