Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level.

Importance: Identifying the optimal long-term biologic therapy for patients with psoriasis is often done through trial and error. Objective: To identify the optimal biologic therapy for individual patients with psoriasis using predictive statistical and machine learning models. Design, Setting, and Participants: This population-based cohort study used data from Danish nationwide registries, primarily DERMBIO, and included adult patients treated for moderate-to-severe psoriasis with biologics. Data were processed and analyzed between spring 2021 and spring 2022. Main Outcomes and Measures: Patient clusters of clinical relevance were identified and their success rates estimated for each drug. Furthermore, predictive prognostic models to identify optimal biologic treatment at the individual level based on data from nationwide registries were evaluated. Results: Assuming a success criterion of 3 years of sustained treatment, this study included 2034 patients with a total of 3452 treatment series. Most treatment series involved male patients (2147 [62.2%]) originating from Denmark (3190 [92.4%]), and 2414 (69.9%) had finished an education longer than primary school. The average ages were 24.9 years at psoriasis diagnosis and 45.5 years at initiation of biologic therapy. Gradient-boosted decision trees and logistic regression were able to predict a specific cytokine target (eg, interleukin-17 inhibition) associated with a successful treatment with accuracies of 63.6% and 59.2%, and top 2 accuracies of 95.9% and 93.9%. When predicting specific drugs resulting in success, gradient boost and logistic regression had accuracies of 48.5% and 44.4%, top 2 accuracies of 77.6% and 75.9%, and top 3 accuracies of 89.9% and 89.0%. Conclusions and Relevance: Of the treatment prediction models used in this cohort study of patients with psoriasis, gradient-boosted decision trees performed significantly better than logistic regression when predicting specific biologic therapy (by drug as well as target) leading to a treatment duration of at least 3 years without discontinuation. Predicting the optimal biologic could benefit patients and clinicians by minimizing the number of failed treatment attempts.

Bumepamine, a brain-permeant benzylamine derivative of bumetanide, does not inhibit NKCC1 but is more potent to enhance phenobarbital's anti-seizure efficacy.

Based on the potential role of Na-K-Cl cotransporters (NKCCs) in epileptic seizures, the loop diuretic bumetanide, which blocks the NKCC1 isoforms NKCC1 and NKCC2, has been tested as an adjunct with phenobarbital to suppress seizures. However, because of its physicochemical properties, bumetanide only poorly penetrates through the blood-brain barrier. Thus, concentrations needed to inhibit NKCC1 in hippocampal and neocortical neurons are not reached when using doses (0.1-0.5 mg/kg) in the range of those approved for use as a diuretic in humans. This prompted us to search for a bumetanide derivative that more easily penetrates into the brain. Here we show that bumepamine, a lipophilic benzylamine derivative of bumetanide, exhibits much higher brain penetration than bumetanide and is more potent than the parent drug to potentiate phenobarbital's anticonvulsant effect in two rodent models of chronic difficult-to-treat epilepsy, amygdala kindling in rats and the pilocarpine model in mice. However, bumepamine suppressed NKCC1-dependent giant depolarizing potentials (GDPs) in neonatal rat hippocampal slices much less effectively than bumetanide and did not inhibit GABA-induced Ca2+ transients in the slices, indicating that bumepamine does not inhibit NKCC1. This was substantiated by an oocyte assay, in which bumepamine did not block NKCC1a and NKCC1b after either extra- or intracellular application, whereas bumetanide potently blocked both variants of NKCC1. Experiments with equilibrium dialysis showed high unspecific tissue binding of bumetanide in the brain, which, in addition to its poor brain penetration, further reduces functionally relevant brain concentrations of this drug. These data show that CNS effects of bumetanide previously thought to be mediated by NKCC1 inhibition can also be achieved by a close derivative that does not share this mechanism. Bumepamine has several advantages over bumetanide for CNS targeting, including lower diuretic potency, much higher brain permeability, and higher efficacy to potentiate the anti-seizure effect of phenobarbital.