Biology subjects

Adhikarla, V.

Publications and source records attributed to Adhikarla, V..

8 recordsLinked to original sources

bioRxiv2026

Intranasal L-MYC Neural Stem Cells Restore Neurovascular and Neuroimmune Homeostasis After TBI via CX3CL1 Upregulation and ICAM-1 Downregulation

Traumatic brain injury (TBI) triggers persistent neuroimmune and neurovascular dysfunction that contributes to secondary brain injury and limits recovery. Here, we investigated whether a single intranasal dose of LMNSC01 could reprogram neuroinflammatory and neurovascular responses post TBI and whether these effects were conserved across biological sex. Immunocompetent male and female rats underwent a controlled cortical impact (CCI) or Sham surgery and 7 days later received a single bolus dose of LMNSC01 (6 x10^6 cells/dose) or vehicle. Integrated histological and transcriptomic analyses and multiplex tissue ELISA were used to characterize injury- and treatment-associated responses. CCI induced persistent but sex-dependent transcriptional changes. Male rats exhibited prominent innate immune, adhesion, extracellular matrix, and tissue remodeling responses, whereas females demonstrated broader activation of microglial, interferon, complement, MHC/antigen presentation, and humoral/adaptive immune pathways. LMNSC01 treatment reprogrammed these distinct injury states through partially sex-specific mechanisms. In males, LMNSC01 increased endogenous immune-regulatory mediators, including Il1rn, and reduced pathways associated with adhesion and immune cell trafficking. In females, treatment decreased expression of genes related to microglial, leukocyte, MHC/antigen presentation, cytokine, and adhesion pathways, and reduced macrophage- and CD45 positive cell signatures. Despite these sex-dependent transcriptional changes, LMNSC01 produced conserved protein changes in both sexes, reducing injury associated ICAM-1 and restoring CX3CL1 proteins, proposing a novel molecular mechanism of LMNSCs modulating endothelial activation and neuron-microglia communication post injury. Our study provides further insight into sex differences in responses to injury and treatment after TBI and highlights candidate repair mechanisms, supporting further development of LMNSC01 as a minimally invasive therapy for TBI.

neuroscience↗

bioRxiv2026

Radioligand therapy in combination with CAR T cells overcomes the heterogeneous immunosuppressive prostate tumor microenvironment

177Lu-PSMA-617 (PluvictoTM, Lu-177 RLT) is an FDA-approved targeted radioligand therapy (RLT) for metastatic castration-resistant prostate cancer (mCRPC), but its durability of response to this singular approach poses a challenge to the field. Chimeric antigen receptor (CAR) T cell therapy has revolutionized clinical practice for hematological malignancies, but its clinical development for solid tumors, including mCRPC, has been encumbered by antigen heterogeneity and the immunosuppressive tumor microenvironment (TME). Here, we evaluate the therapeutic combination of Lu-177 RLT and PSCA-CAR T cells to overcome these barriers. In human xenograft and mouse syngeneic prostate cancer models with homogeneous or heterogeneous antigen expression, the sequential administration of Lu-177 RLT, cyclophosphamide (Cy), and PSCA-CAR T cells improves tumor control and prolongs survival compared to monotherapies. Mechanistically, Lu-177 RLT alone or with Cy remodels the TME by promoting pro-inflammatory myeloid responses and activating endogenous T cells, while enhancing CAR T cell activation and effector function. We additionally evaluated 225Ac-PSMA-617 RLT as an emerging approach in combination with CAR T cells and observed anti-tumor responses, supporting its potential as an alternative RLT partner. These findings support RLT as an immune priming strategy to enhance CAR T cell therapy and provide a rationale for clinical translation of this combination in mCRPC. One Sentence SummaryCombining 177Lu-PSMA-617 radioligand therapy with PSCA-CAR T cells improves tumor control and survival in prostate cancer models by overcoming the antigen heterogeneity and reshaping the immunosuppressive tumor microenvironment.

cancer biology↗

bioRxiv2025

Mathematical Modeling of Neural Stem Cell Migration within Brain using Multi-Fiber Tractography

An agent-based model of therapeutic neural stem cell (NSC) migration is developed and used to predict the migration of NSCs in naive mouse brain. The model utilizes generalized q-sampling imaging which resolves white matter fibers that cross in the brain and is shown to better account for variations in NSC migration patterns as compared to diffusion tensor imaging. In calibrating the model to experimental data, we show that the model is able to reproduce the distribution of NSCs in the mouse brain. In addition, we show that the distribution of NSCs in the mouse brain is sensitive to the location of NSC injection. Persistent distribution of NSCs to the olfactory bulb, consistent with developmental pathways including the rostral migratory stream, suggests that future models of therapeutic NSCs in the naive brain may need to include other factors such as chemotaxis or blood flow to account for variations in NSC migration paths. The results highlight the usefulness of the model in predicting which injection locations may provide the best distribution of NSCs to a given target location.

systems biology↗

bioRxiv2024

Mathematical Modeling Unveils Optimization Strategies for Targeted Radionuclide Therapy of Blood Cancers

Targeted radionuclide therapy is based on injections of cancer-specific molecules conjugated with radioactive nuclides. Despite the specificity of this treatment, it is not devoid of side-effects limiting its use and is especially harmful for rapidly proliferating organs well perfused by blood, like bone marrow. Optimization of radioconjugates administration accounting for toxicity constraints can increase treatment efficacy. Based on our experiments on disseminated multiple myeloma mouse model treated by 225Ac-DOTA-daratumumab, we developed a mathematical model which investigation highlighted the following principles for optimization of targeted radionuclide therapy. 1) Nuclide to antibody ratio importance. The density of radioconjugates on cancer cells determines the density of radiation energy deposited in them. Low labeling ratio as well as accumulation of unlabeled antibodies and antibodies attached to decay products in the bloodstream can mitigate cancer radiation damage due to excessive occupation of specific receptors by antibodies devoid of radioactive nuclides. 2) Cancer binding capacity-based dosing. The rate of binding of drug to cancer cells depends on the total number of their specific receptors, which therefore can be estimated from the pharmacokinetic curve of diagnostic radioconjugates. Injection of doses significantly exceeding cancer binding capacity should be avoided since radioconjugates remaining in the bloodstream have negligible efficacy to toxicity ratio. 3) Particle range-guided multi-dosing. The use of short-range particle emitters and high-affinity antibodies allows for robust treatment optimization via initial saturation of cancer binding capacity, enabling redistribution of further injected radioconjugates and deposited dose towards still viable cells that continue expressing specific receptors. SignificanceMathematical modeling yields general principles for optimization of targeted radionuclide therapy in mouse models of multiple myeloma that can be extrapolated on another cancer models and on clinical setting.

cancer biology↗

bioRxiv2022

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics.

cancer biology↗

bioRxiv2019

A mathematical modeling approach to explore kinetics of Chimeric Antigen Receptor (CAR) T-cell Response in glioma: the CARRGO model

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of hematological cancers and is currently being investigated for solid tumors including high-grade glioma brain tumors. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumors. In this work we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor t-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, CAR T-cell proliferation and exhaustion, and CAR T-cell persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyzer to parameterize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted as compared to higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumor growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumor characteristics including tumor growth rate and antigen level to maximize therapeutic benefit. Statement of SignificanceWe utilize a mathematical model to deconvolute the nonlinear contributions of CAR T-cell proliferation and exhaustion to predict therapeutic efficacy and dependence on CAR T-cell dose and target antigen levels.

immunology↗

bioRxiv2018

Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer

Ovarian cancer has the highest mortality rate of all gynecologic cancers, which may be attributed to an often late stage diagnosis, when the cancer is already metastatic, and rapid development of treatment resistance. We propose that the metastatic disease could be better characterized by observing interactions within the microenvironmental niche of the primary site that shapes the tumors early phenotypic progression. We present a mechanistic mathematical model of ovarian cancer that considers spatial interactions between tumor cells and several key stromal components. We demonstrate how spatial biomarker imaging data from the primary tumor can be analyzed to define a patient-specific microenvironment in the mathematical model. We then show preliminary results, using this model, that demonstrate how differences in the niche composition of a tumor affects phenotypic evolution and treatment response.

cancer biology↗

bioRxiv2017

Aging in a relativistic biological space-time

Here we present a theoretical and mathematical perspective on the process of aging. We extend the concepts of physical space and time to an abstract, mathematically-defined space, which we associate with a concept of \"biological space-time\" in which biological clocks operate. We hypothesize that biological dynamics, represented as trajectories in biological space-time, may be used to model and study different rates of biological aging. As a consequence of this hypothesis, we show how the dilation or contraction of time resulting from accelerated or decelerated biological dynamics may be used to study precipitous or protracted aging. We show specific examples of how these principles may be used to model different rates of aging, with an emphasis on cancer in aging. We discuss the implications of this theory, including novel concepts that may improve our interpretation of biological aging.

cancer biology↗