Biology subjects

Dixit, V.

Publications and source records attributed to Dixit, V..

4 recordsLinked to original sources

bioRxiv2025

Delivery of caspase inhibitors through GSDMD pores to inhibit pyroptosis

Caspase-1, -4, -5, and -11 activate Gasdermin D (GSDMD) to form pores in the plasma membrane. In addition to releasing interleukin (IL)-1{beta} and IL-18, GSDMD pores cause a lytic, proinflammatory form of cell death called pyroptosis. Blocking this pathway holds therapeutic promise for the treatment of inflammatory disorders, but clinical trials of cell permeable caspase inhibitors have been unsuccessful. Here, we describe covalent inhibitors of proinflammatory caspases that are impermeable to healthy cells but effectively block caspase-1 driven pyroptosis and IL-1{beta} secretion. Their failure to inhibit apoptosis implies inhibitor entry through GSDMD pores. Propidium iodide entered rescued cells, confirming transient membrane permeabilization via caspase-1 and GSDMD. Caspase-1 inhibition prevented rather than delayed cell death, likely due to the activation of membrane repair mechanisms neutralizing the initial GSDMD pores. Notably, inhibiting caspase-1 and -11 suppressed IL-1{beta} and IL-18 production in a mouse model of endotoxic shock. These findings underscore the therapeutic potential of exploiting GSDMD pores for the delivery of caspase inhibitors, offering a novel strategy for treating inflammatory diseases.

cell biology↗

bioRxiv2021

Efficient Precision Dosing Under Estimated Uncertainties via Koopman Expectations of Bayesian Posteriors with Pumas

Personalized precision dosing is about mathematically determining effective dosing strategies that optimize the probability of containing a patients outcome within a therapeutic window. However, the common Monte Carlo approach for generating patient statistics is computationally expensive because thousands of simulations need to be computed. In this manuscript we describe a new method which utilizes the Koopman operator to perform a direct computation of expected patient outcomes with respect to quantified uncertainties of Bayesian posteriors in a nonlinear mixed effect model framework. We detail how quantities such as the probability of being within the therapeutic window can be calculated with a choice of loss function on the Koopman expectation. We demonstrate a high performance parallelized implementation of this methodology in Pumas(R) and showcase the ability to accelerate the computation of these expectations by 50x-200x over Monte Carlo. We showcase how dosing can be optimized with respect to probabilistic statements respecting variable uncertainties using the Koopman operator. We end by demonstrating an end-to-end workflow, from estimating variable uncertainties via Bayesian estimation to optimizing a dose with respect to parametric uncertainty.

pharmacology and toxicology↗

bioRxiv2020

Accelerated Predictive Healthcare Analytics with Pumas, A High Performance Pharmaceutical Modeling and Simulation Platform

Pharmacometric modeling establishes causal quantitative relationships between administered dose, tissue exposures, desired and undesired effects and patients risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. However, pharmacometric tools have not been designed to handle todays heterogeneous big data and complex models. We set out to design a platform that facilitates domain-specific modeling and its integration with modern analytics to foster innovation and readiness in healthcare. Pumas demonstrates estimation methodologies with dramatic performance advances. New ODE solver algorithms, such as coeficient-optimized higher order integrators and new automatic stiffness detecting algorithms which are robust to frequent discontinuities, give rise to a median 4x performance improvement across a wide range of stiff and non-stiff systems seen in pharmacometric applications. These methods combine with JIT compiler techniques, such as statically-sized optimizations and discrete sensitivity analysis via forward-mode automatic differentiation, to further enhance the accuracy and performance of the solving and parameter estimation process. We demonstrate that when all of these techniques are combined with a validated clinical trial dosing mechanism and non-compartmental analysis (NCA) suite, real applications like NLME fitting see a median 81x acceleration while retaining the same accuracy. Meanwhile in areas with less prior software optimization, like optimal experimental design, we see orders of magnitude performance enhancements over competitors. Further, Pumas combines these technical advances with several workflows that are automated and designed to boost productivity of the day-to-day user activity. Together we show a fast pharmacometric modeling framework for next-generation precision analytics.

pharmacology and toxicology↗

bioRxiv2019

The Gag Protein PEG10 Binds to RNA and Regulates Trophoblast Stem Cell Lineage Specification.

Peg10 (paternally expressed gene 10) is an imprinted gene that is essential for placental development. It is thought to derive from a Ty3-gyspy LTR (long terminal repeat) retrotransposon and retains Gag and Pol-like domains. Here we show that the Gag domain of PEG10 can promote vesicle budding similar to the HIV p24 Gag protein. Expressed in a subset of mouse endocrine organs in addition to the placenta, PEG10 was identified as a substrate of the deubiquitinating enzyme USP9X. Consistent with PEG10 having a critical role in placental development, PEG10-deficient trophoblast stem cells (TSCs) exhibited impaired differentiation into placental lineages. PEG10 expressed in wild-type, differentiating TSCs was bound to many cellular RNAs including Hbegf (Heparin-binding EGF-like growth factor), which is known to play an important role in placentation. Expression of Hbegf was reduced in PEG10-deficient TSCs suggesting that PEG10 might bind to and stabilize RNAs that are critical for normal placental development.

molecular biology↗