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At least 19 recordsLinked to original sources
PubMed2026

GENKI: A generative framework for scalable and robust metabolic kinetic modeling.

GENKI (Generative ENsemble KPI-Informed) is a variational autoencoder-based framework for large-scale kinetic modeling of metabolism. Developed for metabolic engineering applications, GENKI is designed to improve the recovery of kinetically feasible models that reproduce experimentally observed phenotypes under genetic and environmental perturbations. The framework is trained on feasible kinetic model ensembles and uses phenotype-based key performance indicators (KPIs), derived from multi-omics and bioprocess data, to label and enrich models according to their agreement with mutant and condition-specific observations. This enables targeted generation of biologically relevant parameter sets with improved predictive performance. Crucially, GENKI recovers kinetic parameter sets that jointly reproduce wild-type and multiple perturbed physiologies within a single model. We apply GENKI to large-scale kinetic models of Escherichia coli and Saccharomyces cerevisiae under enzyme perturbations and oxygen shifts. In both systems, GENKI enriches kinetic ensembles with models that more accurately reproduce experimentally observed physiologies across multiple perturbations and conditions. GENKI therefore provides a practical framework for perturbation-aware kinetic model refinement within iterative Design-Build-Test-Learn workflows.

DBTL↗
PubMed2026

Kinetic model of a determinate legume root nodule reveals plant metabolic characteristics for more efficient nitrogen fixation symbiosis.

While nitrogen fertilizers are widely used in agricultural production, their application incurs significant environmental and energetic costs. In contrast, some crops are less dependent on these fertilizers because they engage in symbioses with rhizobia, nitrogen-fixing bacteria that provide ammonium to the plant in exchange for carbon. However, the carbon cost associated with nitrogen fixation can negatively impact crop yields. Improving the efficiency of this metabolic process could alleviate this impact on crop productivity. Mathematical models can help us quantitatively explore metabolic behavior and identify potential targets for metabolic engineering. In this work, we developed a kinetic model of determinate root nodule metabolism, where this symbiotic exchange of carbon from the plant and nitrogen from the bacteria occurs. We used this model to evaluate how the predicted metabolic behavior differs between inefficient and efficient nodules, and to identify potential engineering targets for improving nitrogen fixation efficiency and rate. We show that the enzymes phosphoenolpyruvate carboxylase and pyruvate kinase have significant influence on the predicted rate and efficiency of nitrogen fixation, especially when their expression is varied in combination with oxidative Pentose Phosphate Pathway enzymes like glucose-6-phosphate dehydrogenase and 6-phosphogluconolactonase. The model predicts that pairing a 3-fold decrease in glucose-6-phosphate dehydrogenase activity along with either a 3-fold increase in phosphoenolpyruvate carboxylase activity or decrease in pyruvate kinase activity could increase nitrogen fixation rate by 8.82% while improving nitrogen fixation efficiency by 10.99%.

Enzyme kinetics↗
PubMed2026

Kinetic model of E-P condensates dynamics reveals transcriptional speed, noise, and energy trade-offs.

Gene regulation emerges from the interplay between chromatin architecture and the molecular interactions that connect enhancers to promoters. To study how these interactions shape transcriptional dynamics, we developed a kinetic model that incorporates multivalent enhancer-promoter binding, transcription factor competition, steric constraints, and chromatin accessibility. The model shows that competition among regulatory factors can generate bistable promoter states at the expense of increased transcriptional noise, revealing a direct relationship between bistability and noise levels. It further predicts that promoter response times are fastest in parameter regimes where bistability appears, suggesting that regulatory dynamics supporting two promoter states may intrinsically enable rapid activation. Extending this analysis, we find that intermediate chromatin accessibility and competition between activators and repressors both promote bistable enhancer-bound promoter clusters and fast switching at the cost of higher noise, whereas very high or low accessibility and noncompetitive transcription factors result in monostable expression states. The model also offers a quantitative framework to compare the energetic costs of different regulatory strategies, indicating that, under energy constraints, cells may favor adjusting transcription factor concentrations rather than altering chromatin accessibility, thereby linking energy expenditure to regulatory flexibility and robustness.

Kinetics↗
PubMed2025

Biomathematical enzyme kinetics model of prebiotic autocatalytic RNA networks: degenerating parasite-specific hyperparasite catalysts confer parasite resistance and herald the birth of molecular immunity.

Catalysis and specifically autocatalysis are the quintessential building blocks of life. Yet, although autocatalytic networks are necessary, they are not sufficient for the emergence of life-like properties, such as replication and adaptation. The ultimate and potentially fatal threat faced by molecular replicators is parasitism; if the polymerase error rate exceeds a critical threshold, even the fittest molecular species will disappear. Here we have developed an autocatalytic RNA early life mathematical network model based on enzyme kinetics, specifically the steady-state approximation. We confirm previous models showing that these second-order autocatalytic cycles are sustainable, provided there is a sufficient nucleotide pool. However, molecular parasites become untenable unless they sequentially degenerate to hyperparasites (i.e. parasites of parasites). Parasite resistance-a parasite-specific host response decreasing parasite fitness-is acquired gradually, and eventually involves an increased binding affinity of hyperparasites for parasites. Our model is supported at three levels; firstly, ribozyme polymerases display Michaelis-Menten saturation kinetics and comply with the steady-state approximation. Secondly, ribozyme polymerases are capable of sustainable auto-amplification and of surmounting the fatal error threshold. Thirdly, with growing sequence divergence of host and parasite catalysts, the probability of self-binding is expected to increase and the trend towards cross-reactivity to diminish. Our model predicts that primordial host-RNA populations evolved via an arms race towards a host-parasite-hyperparasite catalyst trio that conferred parasite resistance within an RNA replicator niche. While molecular parasites have traditionally been viewed as a nuisance, our model argues for their integration into the host habitat rather than their separation. It adds another mechanism-with biochemical precision-by which parasitism can be tamed and offers an attractive explanation for the universal coexistence of catalyst trios within prokaryotes and the virosphere, heralding the birth of a primitive molecular immunity.

Kinetics↗
PubMed2025

Mechanisms of enhanced or impaired DNA target selectivity driven by protein dimerization.

Successful DNA transcription demands coordination between proteins that bind DNA while simultaneously binding to one another to form dimers or higher-order complexes. For proteins with numerous DNA targets throughout the genome, measurements that report on their dwell time or occupancy thus represent a convolution over a population interacting with specific DNA, nonspecific DNA, or protein partners on DNA. Dimerization is known to add contacts that can help a single protein to stably bind DNA. However, we show here that dimerization can also impair measured dwell times and occupancy on target sequences because the population redistributes across DNA. We combine mass-action kinetic models of pairwise reversible reactions between proteins and DNA with theory and spatial stochastic simulations to isolate the role of dimerization on observed DNA dwell times, occupancy, and spatial distribution of proteins on DNA. Three key themes emerge: (i) Protein-protein interactions, in addition to protein-DNA interactions, can localize a protein to DNA, and relative binding rates can thus widely tune dwell times. (ii) Dimensional reduction achieved through nonspecific binding and subsequent 1D diffusion controls the order-of-magnitude of enhancements despite nucleosome barriers. (iii) Dimerization enhances selectivity for locally clustered targets and often impairs binding to widely-spaced targets by sequestration. Compared with ChIP-seq data, our model explains how the distribution of the essential GAF protein throughout the genome is highly selective for clustered targets due to protein interactions. This model framework predicts when even weak dimerization can redistribute and stabilize proteins on DNA as a necessary part of transcription.

DNA binding↗
PubMed2025

Oligomerization and positive feedback on membrane recruitment encode dynamically stable PAR-3 asymmetries in the C. elegans zygote.

Studies of PAR polarity have emphasized a paradigm in which mutually antagonistic PAR proteins form complementary polar domains in response to transient cues. A growing body of work suggests that the oligomeric scaffold PAR-3 can form unipolar asymmetries without mutual antagonism, but how it does so is largely unknown. Here we combine single molecule analysis and modeling to show how the interplay of two positive feedback loops promotes dynamically stable unipolar PAR-3 asymmetries in early C. elegans embryos. First, the intrinsic dynamics of PAR-3 membrane binding and oligomerization encode negative feedback on PAR-3 dissociation. Second, membrane-bound PAR-3 promotes its own recruitment through a mechanism that requires the anterior polarity proteins PAR-6 and PKC-3. Using a kinetic model tightly constrained by our experimental measurements, we show that these two feedback loops are individually required and jointly sufficient to encode dynamically stable and locally inducible unipolar PAR-3 asymmetries in the absence of posterior inhibition. Given the central role of PAR-3, and the conservation of PAR-3 membrane-binding, oligomerization, and core interactions with PAR-6/PKC-3, these results have widespread implications for PAR-mediated polarity in metazoa.

Journal Article↗
PubMed2025

Yeast growth is controlled by the proportional scaling of mRNA and ribosome concentrations.

Despite growth being fundamental to all aspects of cell biology, we do not yet know its organizing principles in eukaryotic cells. Classic models derived from the bacteria E. coli posit that protein-synthesis rates are set by mass-action collisions between charged tRNAs produced by metabolic enzymes and mRNA-bound ribosomes. These models show that faster growth is achieved by simultaneously raising both ribosome content and peptide elongation speed. Here, we test if these models are valid for eukaryotes by combining single-molecule tracking, spike-in RNA sequencing, and proteomics in 15 carbon- and nitrogen-limited conditions using the budding yeast S. cerevisiae. Ribosome concentration increases linearly with growth rate, as in bacteria, but the peptide elongation speed remains constant (~9 amino acids/s) and charged tRNAs are not limiting. Total mRNA concentration rises in direct proportion to ribosomes, driven by enhanced RNA polymerase II occupancy of the genome. We show that a simple kinetic model of mRNA-ribosome binding predicts both the fraction of active ribosomes, the growth rate, and responses to transcriptional perturbations. Yeast accelerate growth by coordinately and proportionally co-up-regulating total mRNA and ribosome concentrations, not by speeding elongation. Taken together, our work establishes a new framework for eukaryotic growth control and resource allocation.

Journal Article↗
PubMed2026

Exploring biohydrogen producing potential of Arctic ice and water through metagenomics and dark fermentation kinetics.

Cryospheric ecosystems in the high Arctic harbor largely unexplored microbiomes with significant biotechnological potential. The present study evaluates the biohydrogen production capabilities of the indigenous microbiome of Ny-Ålesund, Svalbard, using glacial ice and surface water samples. Dark fermentation batch assays were performed at 4 °C and 20 °C with 2-bromoethanesulfonate (BES), a methanogenic inhibitor, to track the succession of metabolic and taxonomic diversity. Metagenomic and functional analyses revealed that under 20 °C and BES conditions, psychrotolerant microbial communities maximize biohydrogen production to 85% of the total biogas produced, with an acetate-dominant fermentation pathway, as inferred from volatile fatty acid (VFA) analysis. This evolves into a highly coordinated system utilizing a coupled Rnf-nitrogenase route alongside Formate Hydrogenlyase and [FeFe]-hydrogenase pathways. Kinetic modelling using the Modified Gompertz equation, along with Q10 temperature-sensitivity indices, demonstrated a very high latent catalytic potential in these cold-adapted microbiomes. This study indicates that Arctic microbiomes are highly elastic thermodynamically and could serve as highly efficient, manipulatable biocatalysts for the environmental recovery of bioenergy through engineered low-temperature systems.

Fermentation↗
PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed‑batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial‑and‑error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell‑specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome‑scale metabolic flux sampling analysis revealed that low‑CSPR and sodium butyrate induce a convergent up‑regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth‑kinetic model for the combined low‑CSPR + butyrate strategy, incorporating parameter uncertainty. This model‑guided framework enabled the rational design of two distinct high‑productivity perfusion processes: a sustained mode that achieved robust long‑term stability alongside substantial productivity gains, and a high‑intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof‑of‑concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗
PubMed2025

Modeling Early-Onset Cancer Kinetics Reveals Changes in Underlying Risk and the Impact of Population Screening.

UNLABELLED: Recent studies have reported increases in early-onset cancer cases (diagnosed less than 50 years of age) and raised questions about whether the increase is related to earlier diagnosis from nonspecific medical tests as reflected by decreasing tumor-size-at-diagnosis (apparent effects) or actual increases in underlying cancer risk (true effects), or both. The classic Multistage Clonal Expansion (MSCE) model assumes cancer detection at the first malignant cell's emergence, although later modifications have included lag-times or stochasticity in detection to represent the delay in tumor detection. In this study, we introduced an approach to explicitly incorporate tumor-size-at-diagnosis in the MSCE framework accounting for improvements in cancer detection over time to distinguish between apparent and true increases in early-onset cancer incidence. The model was structurally identifiable and provided better parameter estimation than the classic model. The model was applied to colorectal, breast, and thyroid cancers to examine changes in cancer risk while accounting for detection improvements over time in three representative birth cohorts (1950-1954, 1965-1969, and 1980-1984). The analyses suggested accelerated carcinogenic events and shorter mean sojourn times (the average time from the first malignant cell emergence to cancer detection) in more recent cohorts. Furthermore, using this model to examine the screening impact on the incidence of breast and colorectal cancers, for which both have established screening protocols, provided results that align with well-documented differences in screening effects between these cancers. These findings underscore the importance of incorporating tumor-size-at-diagnosis in cancer modeling and support true increases in early-onset cancer risk in recent years for breast, colorectal, and thyroid cancers. SIGNIFICANCE: A model of early-onset cancer trends that distinguishes true risk from detection effects accurately captures cancer kinetics, trends in cancer progression, and the impact of screening, which could inform cancer prevention strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗
PubMed2017

Rapid and reversible epigenome editing by endogenous chromatin regulators.

Understanding the causal link between epigenetic marks and gene regulation remains a central question in chromatin biology. To edit the epigenome we developed the FIRE-Cas9 system for rapid and reversible recruitment of endogenous chromatin regulators to specific genomic loci. We enhanced the dCas9-MS2 anchor for genome targeting with Fkbp/Frb dimerizing fusion proteins to allow chemical-induced proximity of a desired chromatin regulator. We find that mSWI/SNF (BAF) complex recruitment is sufficient to oppose Polycomb within minutes, leading to activation of bivalent gene transcription in mouse embryonic stem cells. Furthermore, Hp1/Suv39h1 heterochromatin complex recruitment to active promoters deposits H3K9me3 domains, resulting in gene silencing that can be reversed upon washout of the chemical dimerizer. This inducible recruitment strategy provides precise kinetic information to model epigenetic memory and plasticity. It is broadly applicable to mechanistic studies of chromatin in mammalian cells and is particularly suited to the analysis of endogenous multi-subunit chromatin regulator complexes.Understanding the link between epigenetic marks and gene regulation requires the development of new tools to directly manipulate chromatin. Here the authors demonstrate a Cas9-based system to recruit chromatin remodelers to loci of interest, allowing rapid, reversible manipulation of epigenetic states.

CRISPR-Cas Systems↗
PubMed2026

Enzyme kinetics shapes the growth response of metabolic networks.

Microbes adjust their metabolism to environmental challenges by changing protein expression levels, metabolite concentrations, and reaction rates. Average expression levels in large proteome sectors change coherently, while individual proteins show divergent shifts even within the same pathway. Here, we establish a metabolic model that integrates local enzyme kinetics and global network architecture to predict the joint growth response of proteins and metabolites. Under nutrient limitation, we predict a remarkably simple pattern of proteome reallocation with growth rate: protein expression levels change linearly but heterogeneously. For a given enzyme, the direction of change is determined by its local kinetic constants - catalytic rate and substrate affinity - and by the degree of nutrient restriction affecting its embedding pathway. This double-graded growth response of the proteome is mediated by restriction-dependent metabolite levels, which are predicted to decrease with growth rate in a nonlinear way. The model establishes three specific growth laws: protein expression changes of individual enzymes are negatively correlated with their expression and with their substrate saturation at high growth; average changes of pathways and larger functional sectors are correlated with their internal variance. These predictions are in quantitative agreement with measured system-wide proteomics and metabolomics data of E. coli. Enzyme-specific response patterns are a starting point for model-guided interventions into bacterial metabolism.

Kinetics↗
PubMed2026

Prognostic value of circulating tumor DNA and copy-number alterations in patients receiving tandem [225Ac]Ac-/[177Lu]Lu-PSMA-617 therapy for metastatic castration-resistant prostate cancer: a prospective observational study.

BACKGROUND: Prostate-specific membrane antigen-targeted radioligand therapy (PSMA-RLT) demonstrates clinical efficacy in metastatic castration-resistant prostate cancer (mCRPC), yet robust biomarkers for dynamic treatment monitoring and resistance remain lacking. We investigated circulating tumor DNA (ctDNA)-derived tumor fraction (TFx) and genome-wide copy-number alterations (CNAs) as non-invasive biomarkers of treatment response and resistance biology. METHODS: Seventy-eight patients with advanced mCRPC receiving tandem [225Ac]Ac-/[177Lu]Lu-PSMA-617 were prospectively enrolled. Plasma samples collected longitudinally (n = 172) underwent ultra-low-pass whole-genome sequencing. TFx was estimated using ichorCNA, and recurrent CNAs were identified using GISTIC2.0. Associations with progression and overall survival (OS) were assessed using Cox proportional hazards models, including time-dependent analyses. RESULTS: Baseline TFx differed across metastatic disease stages (p = 0.027) and dynamic TFx changes paralleled PSA kinetics during early treatment. Modelled as a time-dependent variable, TFx was associated with a significantly increased risk of progression (HR 4.9, 95% CI 1.2-20.1, p = 0.026). Unsupervised clustering identified distinct high- and low-CNA burden groups strongly correlated with TFx (p = 8.09 × 10⁻8). High CNA burden was associated with shorter median OS (8.3 vs 13.8 months). Multivariable analysis identified baseline logPSA and logALP as independent predictors of OS. Recurrent CNAs affected key tumor suppressors (PTEN, RB1, BRCA2, ATM) and were enriched in pathways related to TP53 signalling, homologous recombination repair, and oncogenic signaling. Longitudinal analyses demonstrated persistence and expansion of specific amplifications at progression. CONCLUSIONS: ctDNA-derived TFx represents a dynamic biomarker of treatment response and progression risk, while CNA profiling provides insight into resistance mechanisms in mCRPC treated with PSMA-RLT. These findings support the integration of ctDNA-based biomarkers into clinical stratification and real-time monitoring strategies.

Humans↗
PubMed2026

Depression and amyloid-β across CSF, PET, and plasma biomarkers: a systematic review and meta-analysis.

Alzheimer's disease is increasingly defined by biomarker evidence of amyloid-β and tau pathology, sharpening questions about whether late-life depression contributes to, or instead reflects, this pathology. We conducted a systematic review and meta-analysis of studies published between 2000 and 2025 that compared amyloid-β biomarkers in adults with and without depression, with depression defined by validated clinical diagnoses or symptom rating scales. Twenty-four studies were included, spanning three biomarker sources: cerebrospinal fluid, positron emission tomography imaging, and plasma. Across all sources, the pooled difference in amyloid-β burden between depressed and non-depressed individuals was small and clustered near zero, indicating only a weak, statistically non-significant tendency toward higher amyloid in depression. When the three sources were examined separately, each yielded a similar near-null result, although between-study heterogeneity was considerable for cerebrospinal fluid and plasma and moderate for imaging. Importantly, a prespecified subgroup analysis showed that imaging results diverged by quantification method: studies using the simpler standardized uptake value ratio clustered around zero, whereas the smaller group of studies using kinetic distribution volume ratio modelling showed a significant positive association, suggesting that methodological choices critically influence the observed relationship. Taken together, these findings indicate that depression is not consistently accompanied by greater amyloid-β burden across widely used biomarker platforms. The distribution volume ratio signal nonetheless raises the possibility of subtle associations that cruder methods may obscure, and suggests that depression may shape Alzheimer's disease trajectories more by modifying the clinical impact of amyloid than by altering its amount.

Humans↗
PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗
PubMed2026

Meta-analysis of growth and inactivation kinetics of Legionella.

Quantitative risk assessments intended to inform evidence-based water management plans and public health targets for Legionella in engineered water systems are constrained by fragmented and heterogeneous growth and inactivation kinetics. We conducted a meta-analysis of 25 growth and 39 thermal- and chemical-inactivation studies, fitting microbial persistence models to harmonize parameters. Nonlinear models outperformed first-order formulations, indicating that lag phases and resistant or protected subpopulations are central to Legionella persistence. Random forest analysis identified environmental and methodological drivers of variability based on 226 growth rates and reduction times for thermal (209) and chemical (135) inactivation. Growth was primarily governed by temperature, nutrient availability, and compatible Legionella-host pairings; thermal inactivation by quantification method, temperature, and turbidity; and chemical inactivation by inoculum size, disinfectant type, concentration, and host-associations. Accordingly, temperature-dependent growth parameters and exposure metrics for heat, free-chlorine, and monochloramine, expressed as TT (Temperature×time) and CT (Concentration×time), were derived as condition-specific inputs for predictive models. Growth optima around 37-40 °C, together with lag-time estimates, indicate that hot-water temperature setbacks and energy-saving practices may favor Legionella proliferation under repeated or prolonged lukewarm exposure. Culture- and viability-based TT differences highlight the need to consider viable‑but-non-culturable persistence in monitoring programs. CT comparisons suggest monochloramine may be advantageous because of its lower apparent sensitivity to host-associated protection. Although limited by restricted experimental conditions, the findings show that predictive models should account for microbial ecology, water matrix effects, and quantification endpoints. Future kinetic studies should prioritize realistic multi-host systems, strain pre-adaptation, complementary viability measurements, and standardized protocols and reporting to ensure reproducibility and enable robust system-level predictive modeling.

Legionella↗
PubMed2026

Decoding Nonlinearities in AAV-Based Gene Therapy Using PBPK Modelling.

The objective of this research was to develop a physiologically based pharmacokinetic (PBPK) model for AAV-based gene therapy, which can capture the nonlinearity observed in both viral vector and transgene product pharmacokinetics (PK) across a wide range of doses, while accounting for the effect of immunogenicity. To develop the PBPK model, previously published PK data generated in mice using AAV8 vector containing the transgene for a non-binding monoclonal antibody was used. Immunocompetent mice were administered with AAV at a wide range of doses (1E8, 1E9, 5E9, 1E10, 2E10, 1E11, 2E11, 1E12, and 1E13vg per mouse), and the PK of transgene and transgene product (i.e., antibody) in plasma and/or tissue was collected. The nonlinearity in transgene product concentrations was characterized using a saturable production process and a concentration-dependent antibody elimination rate was used to characterize the effect of anti-drug antibody (ADA) on transgene product. The model successfully described the PK of both the vector and the transgene product across all dose levels and accurately captured the sigmoidal dose-exposure-response relationship for AAV. Notably, the model described a dose-dependent ADA response, with the high dose group exhibiting an earlier onset and faster rate of transgene product elimination. Lower dose group showed delayed onset and minimal ADA-mediated elimination of transgene product. Overall, the PBPK model presented here effectively characterizes vector and transgene product kinetics in mice and demonstrates utility in preclinical-to-clinical translation and dose optimization of AAV-based gene therapies.

Animals↗