Biology subjects

Dixit, P.

Publications and source records attributed to Dixit, P..

7 recordsLinked to original sources

bioRxiv2024

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma

Purpose: This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. Results: In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Simple SummaryCombining drugs is crucial for enhancing anti-cancer responses. However, the potential of pre-clinical data in identifying suitable combinations and dosage is often underutilized. In this study, we leverage preclinical in vitro cell line drug response data and computational modeling of signal transduction and of pharmacokinetics to elucidate distinct dose requirements for the combination of pan-RAF and MEK inhibitors in melanoma. Our findings reveal a more synergistic, but narrower dosing landscape in NRAS vs BRAF mutant melanoma, which we linked to a mechanism of adaptive resistance through negative feedback. Further, our analysis suggests the importance of drug dosing strategies to optimize synergy based on mutational context, yet highlights the real-world challenges of maintaining a narrow dose range. This approach establishes a framework for translational investigation of drug responses in the refinement of combination therapy, balancing the potential for synergy and practical feasibility in cancer treatment planning.

systems biology↗

bioRxiv2024

A quantitative characterization of the heterogeneous response of glioblastoma U-87 MG cell line to temozolomide

Most cancers are genetically and phenotypically heterogeneous. This includes subpopulations of cells with different levels of sensitivity to chemotherapy, which may lead to treatment failure as the more resistant cells can survive drug treatment and continue to proliferate. While the genetic basis of resistance to many drugs is relatively well characterised, non-genetic factors are much less understood. Here we investigate the role of non-genetic, phenotypic heterogeneity in the response of glioblastoma cancer cells to the drug temozolomide (TMZ) often used to treat this type of cancer. Using a combination of live imaging, machine-learning image analysis and agent-based modelling, we show that even if all cells share the same genetic background, individual cells respond differently to TMZ. We quantitatively characterise this response by measuring the doubling time, lifespan, and motility of cells, and determine how these quantities correlate with each other as well as between the mother and daughter cell. We also show that these responses do not correlate with the cellular level of the enzyme MGMT which has been implicated in the response to TMZ.

cancer biology↗

bioRxiv2023

The Quest to Identify USP8 Inhibitors for Parkinsons Disease, a PAINful Experience.

Pan Assay INterference compoundS (PAINS) are known to be a source of false positives in High Throughput Screening (HTS) campaigns. This has become a major problem in medicinal chemistry, often resulting in undesirable project outcomes and increased overall cost. Our recent campaign to identify inhibitors of USP8 that could be used in the treatment of Parkinsons disease identified several PAINS that worked via a variety of mechanisms. Herein, we discuss the process developed to identify not only the PAINS but also confirming the interference mechanism causing their activity. We found in this project that our USP8 assay was susceptible to multiple modes of interference, making it difficult to identify genuine hit molecules. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/556294v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@b917a6org.highwire.dtl.DTLVardef@1bfac47org.highwire.dtl.DTLVardef@c78e1dorg.highwire.dtl.DTLVardef@1434fdd_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2019

Morpho-anatomical variation and their phylogenetic implications in native and exotic species of Pinus growing in the Indian Himalayas.

Pine is native to all continents and some oceanic islands of the northern hemisphere, chiefly in boreal, temperate or mountainous tropical regions; reaching its southernmost distribution below the equator in Southeast Asia. Pinus is divided into two subgenera, Strobus, and Pinus by the number of vascular bundles present in the needles. Comprehensive and detailed anatomy of needles in ten species of Pinus using nine anatomical traits was carried out. These morphological and anatomical traits supported the classification of the genus up to section level. It was observed that number of needles per fascicle varied along with other related traits such as thickness and width of vascular bundles, the diameter of resin ducts, the thickness of epidermis and thickness and width of endodermal cells that show remarkable variations among different species selected for the present study. The data can be used as a tool for identification and classification of Pinus upto genus and species level. We also found that similarity and differences in leaf anatomical traits supported the molecular phylogeny of Pinus conducted by several researchers.

plant biology↗

bioRxiv2017

Introducing prescribed biases in out of equilibrium Markov models

Markov models are often used in modeling complex out of equilibrium chemical and biochemical systems. However, many times their predictions do not agree with experiments. We need a systematic framework to update Markov models to make them consistent with constraints that are derived from experiments. Here, we present a framework based on the principle of maximum path entropy to update Markov models using stationary state and dynamical trajectory-based constraints. We illustrate the framework using a biochemical model network of growth factors-based signaling. We also show how to find the closest detailed balanced Markov model to a given Markov model. Further applications and generalizations are discussed.

biophysics↗

bioRxiv2017

Receptor-Based Mechanism of Relative Sensing in Mammalian Signaling Networks

Detecting relative rather than absolute changes in external signals enables cells to make decisions in fluctuating environments and diverse biological contexts. However, how mammalian signaling networks store the memories of past stimuli and use them to compute relative signals is not well understood. Using the growth factor-activated PI3K-Akt signaling pathway, we develop computational and analytical models, and experimentally validate a novel mechanism of relative sensing in mammalian cells. This non-transcriptional mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. We show the robustness and specificity of the relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. The described memory and sensing mechanism could play a role in multiple other sensory cascades where stimulation leads to a proportional reduction in the abundance of cell surface receptors.

systems biology↗

bioRxiv2017

Maximum Entropy Framework For Inference Of Cell Population Heterogeneity In Signaling Networks

Predictive models of signaling networks are essential tools for understanding cell population heterogeneity and designing rational interventions in disease. However, using network models to predict signaling dynamics heterogeneity is often challenging due to the extensive variability of signaling parameters across cell populations. Here, we describe a Maximum Entropy-based fRamework for Inference of heterogeneity in Dynamics of sIgAling Networks (MERIDIAN). MERIDIAN allows us to estimate the joint probability distribution over signaling parameters that is consistent with experimentally observed cell-to-cell variability in abundances of network species. We apply the developed approach to investigate the heterogeneity in the signaling network activated by the epidermal growth factor (EGF) and leading to phosphorylation of protein kinase B (Akt). Using the inferred parameter distribution, we also predict heterogeneity of phosphorylated Akt levels and the distribution of EGF receptor abundance hours after EGF stimulation. We discuss how MERIDIAN can be generalized and applied to problems beyond modeling of heterogeneous signaling dynamics.

systems biology↗