Publications by authors named "Jaydeep Srimani"

Multiple myeloma management requires a balance between maximizing survival, minimizing adverse events to therapy, and monitoring disease progression. While previous work has proposed data-driven models for individual tasks, these approaches fail to provide a holistic view of a patient's disease state, limiting their utility to assist physician decision-making. To address this limitation, we developed a transformer-based machine learning model that jointly (1) predicts progression-free survival (PFS), overall survival (OS), and adverse events (AE), (2) forecasts key disease biomarkers, and (3) assesses the effect of different treatment strategies, e.

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Novel therapies have improved outcomes for multiple myeloma (MM) patients, but most ultimately relapse, making treatment decisions for relapsed/refractory MM (RRMM) patients increasingly challenging. We report the final analysis of a single-arm, phase 2 study evaluating the oral proteasome inhibitor (PI) ixazomib combined with daratumumab and dexamethasone (IDd; NCT03439293). Sixty-one RRMM patients (ixazomib/daratumumab-naïve; 1-3 prior therapies) were enrolled to receive IDd (28-day cycles) until disease progression/unacceptable toxicity.

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Ixazomib has been approved in several countries as single-agent maintenance therapy in newly diagnosed multiple myeloma, in both posttransplant and transplant-ineligible settings, based on two phase III studies. In these maintenance studies, patients were initially administered 3 mg ixazomib, escalating to 4 mg if the initial dose level was well tolerated through Cycles 1-4. Here, we report the results of exposure-response analyses of TOURMALINE-MM4, wherein relationships between exposure and clinical response, dose adjustments, and selected adverse events were evaluated.

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Valoctocogene roxaparvovec (AAV5-hFVIII-SQ) gene transfer provided reduced bleeding for adult clinical trial participants with severe hemophilia A. However, pediatric outcomes are unknown. Using a mouse model of hemophilia A, we investigated the effect of vector dose and age at treatment on transgene production and persistence.

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Ixazomib is an oral proteasome inhibitor approved in combination with lenalidomide and dexamethasone for the treatment of relapsed/refractory multiple myeloma (MM). Approval in the United States, Europe, and additional countries was based on results from the phase III TOURMALINE-MM1 (C16010) study. Here, joint population pharmacokinetic/pharmacodynamic time-to-event (TTE) and discrete time Markov models were developed to describe key safety (rash and diarrhea events, and platelet counts) and efficacy (myeloma protein [M-protein] and progression-free survival [PFS]) outcomes observed in TOURMALINE-MM1.

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Valoctocogene roxaparvovec (AAV5-hFVIII-SQ) is an adeno-associated virus serotype five gene therapy under investigation for the treatment of hemophilia A. Herein, we assessed the potential for germline transmission of AAV5-hFVIII-SQ in mice. Male B6.

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Adeno-associated virus (AAV)-based gene therapies can restore endogenous factor VIII (FVIII) expression in hemophilia A (HA). AAV vectors typically use a B-domain-deleted FVIII transgene, such as human FVIII-SQ in valoctocogene roxaparvovec (AAV5-FVIII-SQ). Surprisingly, the activity of transgene-produced FVIII-SQ was between 1.

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This Article contains errors in Supplementary Table 3, which are described in the Author Correction associated with this Article. The simulation results in the Article were based on the correct formula and thus the results are not affected by this correction. The errors have not been fixed in the original Article.

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In the absence of antibiotic-mediated selection, sensitive bacteria are expected to displace their resistant counterparts if resistance genes are costly. However, many resistance genes persist for long periods in the absence of antibiotics. Horizontal gene transfer (primarily conjugation) could explain this persistence, but it has been suggested that very high conjugation rates would be required.

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The postantibiotic effect (PAE) refers to the temporary suppression of bacterial growth following transient antibiotic treatment. This effect has been observed for decades for a wide variety of antibiotics and microbial species. However, despite empirical observations, a mechanistic understanding of this phenomenon is lacking.

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It is generally assumed that antibiotics can promote horizontal gene transfer. However, because of a variety of confounding factors that complicate the interpretation of previous studies, the mechanisms by which antibiotics modulate horizontal gene transfer remain poorly understood. In particular, it is unclear whether antibiotics directly regulate the efficiency of horizontal gene transfer, serve as a selection force to modulate population dynamics after such gene transfer has occurred, or both.

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Culturing and measuring bacterial population dynamics are critical to develop insights into gene regulation or bacterial physiology. Traditional methods, based on bulk culture to obtain such quantification, have the limitations of higher cost/volume of reagents, non-amendable to small size of population and more laborious manipulation. To this end, droplet-based microfluidics represents a promising alternative that is cost-effective and high-throughput.

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Bacteria have developed resistance against every antibiotic at a rate that is alarming considering the timescale at which new antibiotics are developed. Thus, there is a critical need to use antibiotics more effectively, extend the shelf life of existing antibiotics and minimize their side effects. This requires understanding the mechanisms underlying bacterial drug responses.

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Numerous bacterial species utilize quorum sensing to communicate, but crosstalk often complicates the dynamics of mixed populations. In this issue of Chemistry & Biology, Wu and colleagues take advantage of synthetic gene circuits to elucidate interactions between two quorum sensing systems, with potential applications to fields from infectious diseases to biosynthesis.

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A body of evidence has shown that the control of E2F transcription factor activity is critical for determining cell cycle entry and cell proliferation. However, an understanding of the precise determinants of this control, including the role of other cell-cycle regulatory activities, has not been clearly defined. Here, recognizing that the contributions of individual regulatory components could be masked by heterogeneity in populations of cells, we model the potential roles of individual components together with the use of an integrated system to follow E2F dynamics at the single-cell level and in real time.

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Many cellular decision processes, including proliferation, differentiation, and phenotypic switching, are controlled by bistable signaling networks. In response to transient or intermediate input signals, these networks allocate a population fraction to each of two distinct states (e.g.

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Dispersal is necessary for spread into new habitats, but it has also been shown to inhibit spread. Theoretical studies have suggested that the presence of a strong Allee effect may account for these counterintuitive observations. Experimental demonstration of this notion is lacking due to the difficulty in quantitative analysis of such phenomena in a natural setting.

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Quorum sensing (QS) enables bacteria to sense and respond to changes in their population density. It plays a critical role in controlling different biological functions, including bioluminescence and bacterial virulence. It has also been widely adapted to program robust dynamics in one or multiple cellular populations.

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The inoculum effect (IE) refers to the decreasing efficacy of an antibiotic with increasing bacterial density. It represents a unique strategy of antibiotic tolerance and it can complicate design of effective antibiotic treatment of bacterial infections. To gain insight into this phenomenon, we have analyzed responses of a lab strain of Escherichia coli to antibiotics that target the ribosome.

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We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results.

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Drug bioavailability is a major failing point of new pharmaceuticals i.e. drugs fail to reach their target or fail to stay there long enough for therapeutic effect.

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