Behavioural immune landscapes of inflammation.

Transcriptional and proteomic profiling of individual cells have revolutionized interpretation of biological phenomena by providing cellular landscapes of healthy and diseased tissues1,2. These approaches, however, do not describe dynamic scenarios in which cells continuously change their biochemical properties and downstream 'behavioural' outputs3-5. Here we used 4D live imaging to record tens to hundreds of morpho-kinetic parameters describing the dynamics of individual leukocytes at sites of active inflammation. By analysing more than 100,000 reconstructions of cell shapes and tracks over time, we obtained behavioural descriptors of individual cells and used these high-dimensional datasets to build behavioural landscapes. These landscapes recognized leukocyte identities in the inflamed skin and trachea, and uncovered a continuum of neutrophil states inside blood vessels, including a large, sessile state that was embraced by the underlying endothelium and associated with pathogenic inflammation. Behavioural screening in 24 mouse mutants identified the kinase Fgr as a driver of this pathogenic state, and interference with Fgr protected mice from inflammatory injury. Thus, behavioural landscapes report distinct properties of dynamic environments at high cellular resolution.

Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders.

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.

Learning Optimal Dynamic Treatment Regimes from Longitudinal Data.

Studies often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly-robust unbiased transformation of the conditional average treatment effect. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone (BUP-NX) dose to minimize return-to-regular-opioid-use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision making: the learned ODTR outperforms a clinically defined strategy.

Nonparametric causal mediation analysis for stochastic interventional (in)direct effects.

Causal mediation analysis has historically been limited in two important ways: (i) a focus has traditionally been placed on binary exposures and static interventions and (ii) direct and indirect effect decompositions have been pursued that are only identifiable in the absence of intermediate confounders affected by exposure. We present a theoretical study of an (in)direct effect decomposition of the population intervention effect, defined by stochastic interventions jointly applied to the exposure and mediators. In contrast to existing proposals, our causal effects can be evaluated regardless of whether an exposure is categorical or continuous and remain well-defined even in the presence of intermediate confounders affected by exposure. Our (in)direct effects are identifiable without a restrictive assumption on cross-world counterfactual independencies, allowing for substantive conclusions drawn from them to be validated in randomized controlled trials. Beyond the novel effects introduced, we provide a careful study of nonparametric efficiency theory relevant for the construction of flexible, multiply robust estimators of our (in)direct effects, while avoiding undue restrictions induced by assuming parametric models of nuisance parameter functionals. To complement our nonparametric estimation strategy, we introduce inferential techniques for constructing confidence intervals and hypothesis tests, and discuss open-source software, the $\texttt{medshift}$$\texttt{R}$ package, implementing the proposed methodology. Application of our (in)direct effects and their nonparametric estimators is illustrated using data from a comparative effectiveness trial examining the direct and indirect effects of pharmacological therapeutics on relapse to opioid use disorder.

Studying Continuous, Time-varying, and/or Complex Exposures Using Longitudinal Modified Treatment Policies.

This tutorial discusses a methodology for causal inference using longitudinal modified treatment policies. This method facilitates the mathematical formalization, identification, and estimation of many novel parameters and mathematically generalizes many commonly used parameters, such as the average treatment effect. Longitudinal modified treatment policies apply to a wide variety of exposures, including binary, multivariate, and continuous, and can accommodate time-varying treatments and confounders, competing risks, loss to follow-up, as well as survival, binary, or continuous outcomes. Longitudinal modified treatment policies can be seen as an extension of static and dynamic interventions to involve the natural value of treatment and, like dynamic interventions, can be used to define alternative estimands with a positivity assumption that is more likely to be satisfied than estimands corresponding to static interventions. This tutorial aims to illustrate several practical uses of the longitudinal modified treatment policy methodology, including describing different estimation strategies and their corresponding advantages and disadvantages. We provide numerous examples of types of research questions that can be answered using longitudinal modified treatment policies. We go into more depth with one of these examples, specifically, estimating the effect of delaying intubation on critically ill COVID-19 patients' mortality. We demonstrate the use of the open-source R package lmtp to estimate the effects, and we provide code on https://github.com/kathoffman/lmtp-tutorial.

Independent and joint contributions of physical disability and chronic pain to incident opioid use disorder and opioid overdose among Medicaid patients.

BACKGROUND: Chronic pain has been extensively explored as a risk factor for opioid misuse, resulting in increased focus on opioid prescribing practices for individuals with such conditions. Physical disability sometimes co-occurs with chronic pain but may also represent an independent risk factor for opioid misuse. However, previous research has not disentangled whether disability contributes to risk independent of chronic pain. METHODS: Here, we estimate the independent and joint adjusted associations between having a physical disability and co-occurring chronic pain condition at time of Medicaid enrollment on subsequent 18-month risk of incident opioid use disorder (OUD) and non-fatal, unintentional opioid overdose among non-elderly, adult Medicaid beneficiaries (2016-2019). RESULTS: We find robust evidence that having a physical disability approximately doubles the risk of incident OUD or opioid overdose, and physical disability co-occurring with chronic pain increases the risks approximately sixfold as compared to having neither chronic pain nor disability. In absolute numbers, those with neither a physical disability nor chronic pain condition have a 1.8% adjusted risk of incident OUD over 18 months of follow-up, those with physical disability alone have an 2.9% incident risk, those with chronic pain alone have a 3.6% incident risk, and those with co-occurring physical disability and chronic pain have a 11.1% incident risk. CONCLUSIONS: These findings suggest that those with a physical disability should receive increased attention from the medical and healthcare communities to reduce their risk of opioid misuse and attendant negative outcomes.

Using instrumental variables to address unmeasured confounding in causal mediation analysis.

Mediation analysis is a strategy for understanding the mechanisms by which interventions affect later outcomes. However, unobserved confounding concerns may be compounded in mediation analyses, as there may be unobserved exposure-outcome, exposure-mediator, and mediator-outcome confounders. Instrumental variables (IVs) are a popular identification strategy in the presence of unobserved confounding. However, in contrast to the rich literature on the use of IV methods to identify and estimate a total effect of a non-randomized exposure, there has been almost no research into using IV as an identification strategy to identify mediational indirect effects. In response, we define and nonparametrically identify novel estimands-double complier interventional direct and indirect effects-when 2, possibly related, IVs are available, one for the exposure and another for the mediator. We propose nonparametric, robust, efficient estimators for these effects and apply them to a housing voucher experiment.

Predicting Nonradiative Decay Rate Constants of Cyclometalated Ir(III) Complexes.

The theoretical calculation of the temperature-dependent nonradiative decay rate constant is fundamental for predicting the usefulness of transition-metal complexes for technological applications. Such a computation implies the determination of the barriers separating the emitting triplet state from metal-centered states, which are key mediators of this type of radiationless relaxation. We here do so for the two green-emitting cyclometalated Ir(III) complexes, [Ir(ppy)2(pyim)]+ and [Ir(diFppy)2(dtb-bpy)]+, of general formula [Ir(C∧N)2(N∧N)]+, performing DFT calculations with both B3LYP and PBE0 functionals. On the basis of the obtained results and the comparison with the experimental nonradiative decay rate constants, we conclude that B3LYP provides too low energy barriers to the metal-centered states, while the PBE0 provides reasonable values. We consequently recommend to avoid the use of the commonly employed B3LYP functional for the evaluation of such an energy barrier for cyclometalated Ir(III) complexes.

Second generation of pyrimidin-quinolone hybrids obtained from virtual screening acting as sphingosine kinase 1 inhibitors and potential anticancer agents.

We report here the virtual screening design, synthesis and activity of eight new inhibitors of SphK1. For this study we used a pre-trained Graph Convolutional Network (GCN) combined with docking calculations. This exploratory analysis proposed nine compounds from which eight displayed significant inhibitory effect against sphingosine kinase 1 (SphK1) demonstrating a high level of efficacy for this approach. Four of these compounds also displayed anticancer activity against different tumor cell lines, and three of them (5), (6) and (7) have shown a wide inhibitory action against many of the cancer cell line tested, with GI50 below 5 µM, being (5) the most promising with TGI below 10 µM for the half of cell lines. Our results suggest that the three most promising compounds reported here are the pyrimidine-quinolone hybrids (1) and (6) linked by p-aminophenylsulfanyl and o-aminophenol fragments respectively, and (8) without such aryl linker. We also performed an exhaustive study about the molecular interactions that stabilize the different ligands at the binding site of SphK1. This molecular modeling analysis was carried out by using combined techniques: docking calculations, MD simulations and QTAIM analysis. In this study we also included PF543, as reference compound, in order to better understand the molecular behavior of these ligands at the binding site of SphK1.These results provide useful information for the design of new inhibitors of SphK1 possessing these structural scaffolds.

Synthesis of Ethyl Pyrimidine-Quinolincarboxylates Selected from Virtual Screening as Enhanced Lactate Dehydrogenase (LDH) Inhibitors.

The inhibition of the hLDHA (human lactate dehydrogenase A) enzyme has been demonstrated to be of great importance in the treatment of cancer and other diseases, such as primary hyperoxalurias. In that regard, we have designed, using virtual docking screening, a novel family of ethyl pyrimidine-quinolinecarboxylate derivatives (13-18)(a-d) as enhanced hLDHA inhibitors. These inhibitors were synthesised through a convergent pathway by coupling the key ethyl 2-aminophenylquinoline-4-carboxylate scaffolds (7-12), which were prepared by Pfitzinger synthesis followed by a further esterification, to the different 4-aryl-2-chloropyrimidines (VIII(a-d)) under microwave irradiation at 150-170 °C in a green solvent. The values obtained from the hLDHA inhibition were in line with the preliminary of the preliminary docking results, the most potent ones being those with U-shaped disposition. Thirteen of them showed IC50 values lower than 5 µM, and for four of them (16a, 18b, 18c and 18d), IC50 ≈ 1 µM. Additionally, all compounds with IC50 < 10 µM were also tested against the hLDHB isoenzyme, resulting in three of them (15c, 15d and 16d) being selective to the A isoform, with their hLDHB IC50 > 100 µM, and the other thirteen behaving as double inhibitors.