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bioRxiv2026

Sex-differentiated hormonal microenvironments recapitulate in vivo liver metabolism in human iPSC-derived organoids

Bioengineers strive to recreate in vivo microenvironments in vitro to reduce our use of animal models and provide insights into human biology. While liver models show promise, sex differences in liver biology remain largely neglected in preclinical studies. Despite the 2014 EU mandate for the inclusion of women in clinical trials, decoupling of research data by sex is historically rare, with only 11% of papers disaggregating data by sex. This gap contributes to women being more susceptible to drug-induced liver injury (DILI) and being underserved in drug development, as well as to costly drug attrition levels. Here we present a novel approach to modelling sex differences in vitro. Human induced pluripotent stem cells (iPSCs) from both male (XY) and female (XX) donors, were differentiated into hepatocyte liver spheroids and exposed to in vivo-mimicking levels of testosterone, progesterone, and oestrogen in high-throughput microwell format. We successfully recapitulated sex-specific metabolic profiles and demonstrated significant differences in CYP1A2 and CYP3A4 drug metabolism and gene expression patterns consistent with reported in vivo observations, without compromising cell viability. These findings validate the utility of sex-differentiated microenvironments in early-stage research, offering a pathway to refine animal and clinical trials and improve therapeutic outcomes for all sexes.

cell biology↗

bioRxiv2020

A chlorzoxazone-baclofen combination improves cerebellar impairment in spinocerebellar ataxia type 1

BackgroundA combination of central muscle relaxants, chlorzoxazone and baclofen (chlorzoxazone-baclofen), has been proposed for treatment of cerebellar symptoms in human spinocerebellar ataxia (SCA). However, central muscle relaxants can worsen balance. The optimal dose for target engagement without toxicity remains unknown. ObjectivesUsing the genetically precise Atxn1154Q/2Q model of SCA1, we determine the role of cerebellar dysfunction in motor impairment. We also aim to identify appropriate concentrations of chlorzoxazone-baclofen needed for target engagement without toxicity to plan for human clinical trials. MethodsWe use patch clamp electrophysiology in acute cerebellar slices and immunostaining to identify the specific ion channels targeted by chlorzoxazone-baclofen. Behavioral assays for coordination and grip strength are used to determine specificity of chlorzoxazone-baclofen for improving cerebellar dysfunction without off-target effects in Atxn1154Q/2Q mice. ResultsWe identify irregular Purkinje neuron firing in association with reduced expression of the ion channels Kcnma1 and Cacna1g in Atxn1154Q/2Q mice. Using in vitro electrophysiology in brain slices, we identify concentrations of chlorzoxazone-baclofen that improve Purkinje neuron spike regularity without reducing firing frequency. At a disease stage in Atxn1154Q/2Q mice when motor impairment is due to cerebellar dysfunction, orally administered chlorzoxazone-baclofen improves motor performance without affecting muscle strength. ConclusionWe identify a tight relationship between baclofen-chlorzoxazone concentrations needed to engage target, and levels above which cerebellar function will be compromised. We propose to use this information for a novel clinical trial design, using sequential dose escalation within each subject, to identify dose levels that are likely to improve ataxia symptoms while minimizing toxicity.

neuroscience↗

bioRxiv2021

Nicotinamide riboside alleviates Parkinson's disease symptoms but downregulates dopamine metabolism upon lactacystin-induced proteostasis failure

Activation of mitochondrial metabolism and proteostasis with the NAD+ precursor nicotinamide riboside (NR) has emerged as a potential therapeutic approach for neurodegenerative disorders including Parkinsons disease (PD). However, despite recently started clinical trials, studies on NR in animal models of PD are scarce. In this study, we investigated the effect of NR in multiple models of PD. In transgenic C. elegans overexpressing -synuclein, a protein of which aggregation is believed to promote PD, NR rescued PD-like phenotypes including mitochondrial dysfunction and motility defects, decreased oxidative stress, and age-related dopamine (DA) neuron loss. We found that NR eased symptoms of disease by activating the mitochondrial unfolded protein response (UPRmt) via the transcription factor atfs-1. Similarly, in a proteasome inhibitor, lactacystin, -induced mouse model of PD, NR rescued mitochondrial dysfunction and behavioural deficits caused by lactacystin lesion. However, long-term NR supplementation, in conjunction with proteasome inhibition, resulted in decreased DA levels in both the lesioned and unlesioned sides of the substantia nigra with concomitant downregulation of key genes in DA metabolism. Our results suggest specific endpoints that should be monitored in ongoing NR clinical trials.

neuroscience↗

bioRxiv2025

Modeling viral shedding and symptom outcomes in oseltamivir-treated experimental influenza infection

Influenza remains a global public health concern, and although the antiviral drug oseltamivir is widely used to treat infections, questions regarding its actual antiviral efficacy and clinical benefits remain. Here, we evaluated the effects of oseltamivir on viral shedding dynamics in the context of experimental influenza infection. We analyzed individual participant data, including viral load, time to symptom alleviation, and laboratory test measurements, obtained from three publicly available clinical trials involving experimental infections with influenza A and B viruses. We applied mathematical modeling and estimated parameters using a nonlinear mixed-effects model to capture viral infection dynamics. Our analysis revealed that, compared with placebo groups, the oseltamivir-treated groups tended to have lower values in terms of viral load area under the curve, duration of infection, peak viral titer, and time to peak; however, most of these differences were not significant; and no dose-dependent effects were observed. Moreover, there was no significant correlation between time to symptom alleviation and viral load. Some laboratory test parameters showed opposing correlations with symptom-related and viral load-related outcomes. These findings are consistent with distinct mechanisms underlying the symptom-alleviating effects of oseltamivir and its antiviral activity. Our findings suggest that the availability of individual-level data for public use is essential because it enables the evaluation of mechanisms in clinical trials and the development of more appropriate outcome measures.

microbiology↗

bioRxiv2025

Robust prediction of drug combination side effects in realistic settings

Side effects caused by drug combinations pose a major challenge in healthcare. Knowledge of these side effects is limited because often they are not detected in clinical trials, which typically involve a restricted number of participants and tested drug combinations. We introduce DCSE (Drug Combinations Side Effects), a novel machine learning method for predicting polypharmacy side effects. DCSE learns latent signatures for drugs, drug pairs, and side effects to predict the probability that a side effect occurs in a given drug combination. We first evaluate its performance in the commonly adopted experimental settings in the literature. Then, a key contribution of this paper is the introduction of more realistic experimental settings that incorporate warm-start and cold-start scenarios under a prospective evaluation. Here, we attempt to predict side effects reported between 2009 and 2014 after training only on data available prior to that period. Our results indicate that DCSE consistently outperforms state-of-the-art methods, demonstrating its robustness and efficacy in real-world applications. Author summaryUnderstanding the risks of combining medications is a central problem in healthcare. Clinical trials rarely capture the full range of adverse reactions, and many side effects are identified only after drugs reach the market, leaving many possible drug combinations insufficiently tested. Computational approaches can help fill this gap by predicting which combinations are likely to cause specific side effects. Our approach works by learning signatures for both drugs and side effects, which can be thought of as representations that describe how drugs act and what is required to trigger a side effect. A key challenge is how to combine the signatures of two drugs into one representation for the pair. We show that simple linear models fail to capture the complexity of drug interactions, so we use a deep neural network to combine them in a non-linear way. Our tests, which use reports collected at different points in time to mirror how information about drug pairs becomes available, show that our approach is useful for monitoring risks after drugs have been approved and for exploring new uses of pairs of existing drugs.

bioinformatics↗

bioRxiv2019

Prostate-Specific Antigen Dynamics Predict Individual Responses to Intermittent Androgen Deprivation

BackgroundIntermittent androgen deprivation therapy (IADT) is an attractive treatment approach for biochemically recurrent prostate cancer (PCa), whereby cycling treatment on and off can reduce cumulative dose, limit toxicities, and delay development of treatment resistance. To optimize treatment within the context of ongoing intratumoral evolution, underlying mechanisms of resistance and actionable biomarkers need to be identified.\n\nMethodsWe have developed a quantitative framework to simulate enrichment of prostate cancer stem cell (PCaSC) dynamics during treatment as a plausible mechanism of resistance evolution.\n\nResultsSimulated dynamics of PCaSC and non-stem cancer cells demonstrate that stem cell proliferation patterns correlate with longitudinal serum prostate-specific antigen (PSA) measurements in 70 PCa patients undergoing multiple cycles of IADT. By learning the dynamics from each treatment cycle, individual model simulations predict evolution of resistance in the subsequent IADT cycle with a sensitivity and specificity of 57% and 94%, respectively and an overall accuracy of 90%. Additionally, we evaluated the potential benefit of docetaxel for IADT in biochemically recurrent PCa. Model simulations based on response dynamics from the first IADT cycle identify patients who would or would not benefit from concurrent docetaxel in subsequent cycles.\n\nConclusionOur results demonstrate the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics continuously updated with each treatment cycle.\n\nTranslational RelevanceCompared to continuous androgen deprivation therapy, intermittent androgen deprivation (IADT) has been shown to reduce toxicity and delay time to progression in prostate cancer. While numerous mathematical models have been developed to study the response to both continuous and intermittent androgen deprivation, very few have identified actionable biomarkers of resistance and exploited them to predict how patients will or will not respond to subsequent treatment. Here, we identify prostate-specific antigen (PSA) dynamics as the first such biomarker. Mechanistic mathematical modeling of prostate cancer stem cell dynamics that dictate prostate-specific antigen serum levels predicts individual responses to IADT with 90% overall accuracy and can be used to develop patient-specific adaptive treatment protocols, and potentially identify patients that may benefit from concurrent chemotherapy. Model results demonstrate the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics continuously updated with each treatment cycle.

cancer biology↗

bioRxiv2022

Metabolic adaptations underpin resistance to histone acetyltransferase inhibition

Histone acetyltransferases (HAT) catalyze the acylation of lysine side chains and are implicated in diverse human cancers as both oncogenes and non-oncogene dependencies1. Acetyl-CoA-competitive HAT inhibitors have garnered attention as potential cancer therapeutics and the first clinical trial for this class is ongoing (NCT04606446). Despite broad enthusiasm for these targets, notably including CBP/p300 and KAT6A/B2-5, the potential mechanisms of therapeutic response and evolved drug resistance remain poorly understood. Using comparative transcriptional genomics, we found that the direct gene regulatory consequences of CBP/p300 HAT inhibition are indistinguishable in models of intrinsically hypersensitive and insensitive acute myeloid leukemia (AML). We therefore modelled acquired drug resistance using a forward genetic selection and identified dysregulation of coenzyme A (CoA) metabolism as a facile driver of resistance to HAT inhibitors. Specifically, drug resistance selected for mutations in PANK3, a pantothenate kinase that controls the rate limiting step in CoA biosynthesis6. These mutations prevent negative feedback inhibition, resulting in drastically elevated concentrations of intracellular acetyl-CoA, which directly outcompetes drug-target engagement. This not only impacts the activity of structurally diverse CBP/p300 HAT inhibitors, but also agents related to an investigational KAT6A/B inhibitor that is currently in Phase-1 clinical trials. We further validated these results using a genome-scale CRISPR/Cas9 loss-of-function genetic modifier screen, which identified additional gene-drug interactions between HAT inhibitors and the CoA biosynthetic pathway. Top hits from the screen included the phosphatase, PANK4, which negatively regulates CoA production and therefore suppresses sensitivity to HAT inhibition upon knockout7, as well as the pantothenate transporter, SLC5A68, which enhances sensitivity. Altogether, this work uncovers CoA plasticity as an unexpected but potentially class-wide liability of anti-cancer HAT inhibitors and will therefore buoy future efforts to optimize the efficacy of this new form of targeted therapy.

molecular biology↗

bioRxiv2023

NaV1.7 mRNA and protein expression in putative projection neurons of the human spinal dorsal horn

NaV1.7, a membrane-bound voltage-gated sodium channel, is preferentially expressed along primary sensory neurons, including their peripheral & central nerve endings, axons, and soma within the dorsal root ganglia and plays an integral role in amplifying membrane depolarization and pain neurotransmission. Loss- and gain-of-function mutations in the gene encoding NaV1.7, SCN9A, are associated with a complete loss of pain sensation or exacerbated pain in humans, respectively. As an enticing pain target supported by human genetic validation, many compounds have been developed to inhibit NaV1.7 but have disappointed in clinical trials. The underlying reasons are still unclear, but recent reports suggest that inhibiting NaV1.7 in central terminals of nociceptor afferents is critical for achieving pain relief by pharmacological inhibition of NaV1.7. We report for the first time that NaV1.7 mRNA is expressed in putative projection neurons (NK1R+) in the human spinal dorsal horn, predominantly in lamina 1 and 2, as well as in deep dorsal horn neurons and motor neurons in the ventral horn. NaV1.7 protein was found in the central axons of sensory neurons terminating in lamina 1-2, but also was detected in the axon initial segment of resident spinal dorsal horn neurons and in axons entering the anterior commissure. Given that projection neurons are critical for conveying nociceptive information from the dorsal horn to the brain, these data support that dorsal horn NaV1.7 expression may play an unappreciated role in pain phenotypes observed in humans with genetic SCN9A mutations, and in achieving analgesic efficacy in clinical trials.

neuroscience↗

bioRxiv2024

Mechanism-based approach in designing patient-specific combination therapies for nonsense mutation diseases

Premature termination codon (PTC) diseases, arising as a consequence of nonsense mutations in a patients DNA, account for approximately 12% of all human disease mutations. Currently there are no FDA approved treatments for increasing PTC readthrough in nonsense mutation diseases, although one translational readthrough inducing drug, ataluren, has had conditional approval for treatment of Duchenne muscular dystrophy in Europe and elsewhere for 10 years. Ataluren displays consistent low toxicity in clinical trials for treatment of several different PTC diseases, but its therapeutic effects on such diseases are inconsistent. The identity of the stop codon and its sequence context are major determinants of PTC readthrough efficiency in both the absence and presence of nonsense suppressors. Previously we have shown that ataluren stimulates readthrough exclusively by competitively inhibiting release factor complex (RFC, eRF1.eRF3.GTP)-dependent catalysis of translation termination. Here, using an in vitro reconstituted system (PURE-LITE) and both ensemble and single molecule assays, we demonstrate that PTC identity and the immediately adjacent mRNA sequence contexts modulate the catalytic activity of RFC in terminating peptide elongation. Such modulation largely determines the effectiveness of ataluren in stimulating readthrough, whether added alone or in combination with either the aminoglycoside G418 or an anticodon edited aa-tRNA, each of which stimulate readthrough by mechanisms orthogonal to that of ataluren. Our results provide an attractive rationale for the variability of ataluren effectiveness in stimulating readthrough in clinical trials. Patients harboring a PTC mutation with a sequence context promoting strong interaction with RFC are predicted to be resistant to ataluren, whereas ataluren treatment should be more effective for patient sequences conferring weaker interaction with RFC.

biochemistry↗

bioRxiv2023

Cooperativity between H3.3K27M and PDGFRA poses multiple therapeutic vulnerabilities in human iPSC-derived diffuse midline glioma avatars

Diffuse midline glioma (DMG) is a leading cause of brain tumor death in children. In addition to hallmark H3.3K27M mutations, significant subsets also harbor alterations of other genes, such as TP53 and PDGFRA. Despite the prevalence of H3.3K27M, the results of clinical trials in DMG have been mixed, possibly due to the lack of models recapitulating its genetic heterogeneity. To address this gap, we developed human iPSC-derived tumor models harboring TP53R248Q with or without heterozygous H3.3K27M and/or PDGFRAD842V overexpression. The combination of H3.3K27M and PDGFRAD842V resulted in more proliferative tumors when gene-edited neural progenitor (NP) cells were implanted into mouse brains compared to NP with either mutation alone. Transcriptomic comparison of tumors and their NP cells of origin identified conserved JAK/STAT pathway activation across genotypes as characteristic of malignant transformation. Conversely, integrated genome-wide epigenomic and transcriptomic analyses, as well as rational pharmacologic inhibition, revealed targetable vulnerabilities unique to the TP53R248Q; H3.3K27M; PDGFRAD842V tumors and related to their aggressive growth phenotype. These include AREG-mediated cell cycle control, altered metabolism, and vulnerability to combination ONC201/trametinib treatment. Taken together, these data suggest that cooperation between H3.3K27M and PDGFRA influences tumor biology, underscoring the need for better molecular stratification in DMG clinical trials.

genomics↗

bioRxiv2025

BACE1 regulates sleep-wake cycle through both enzymatic and non-enzymatic actions.

The {beta}-secretase BACE1 has become a prime target in Alzheimers disease (AD) therapy, because it drives the production of pathogenic amyloid {beta} peptides. However, clinical trials with BACE1-targeting drugs were halted due to adverse effects on cognitive performance. We propose here that cognitive impairment by BACE1 inhibitors may be a corollary of a higher function of BACE1 related to proper sleep regulation. To address non-enzymatic effects of BACE1 on ion channels likely involved in the sleep-wake cycle, we analyzed sleep patterns in both BACE1-KO mice and a newly generated transgenic line expressing a proteolysis-deficient BACE1 variant (BACE1-KI). We found that BACE1-KI and BACE1-KO mice displayed common and distinct sleep-wake disturbances. Compared to their respective wild-type littermates, both mutant lines slept less during the light phase (when they preferentially rest). Furthermore, transition rates between wake and sleep states were altered, as were sleep spindles and EEG power spectra mainly in the gamma range. Thus, a better understanding of how BACE1 interferes with sleep-modulated behaviors is needed if clinical trials with BACE1-targeted inhibitors are to resume.

animal behavior and cognition↗

bioRxiv2019

Transcriptomic signatures predict regulators of drug synergy and clinical regimen efficacy against Tuberculosis

The rapid spread of multi-drug resistant strains has created a pressing need for new drug regimens to treat tuberculosis (TB), which kills 1.8 million people each year. Identifying new regimens has been challenging due to the slow growth of the pathogen M. tuberculosis (MTB), coupled with large number of possible drug combinations. Here we present a computational model (INDIGO-MTB) that identified synergistic regimens featuring existing and emerging anti-TB drugs after screening in silico over 1 million potential drug combinations using MTB drug transcriptomic profiles. INDIGO-MTB further predicted the gene Rv1353c as a key transcriptional regulator of multiple drug interactions, and we confirmed experimentally that Rv1353c up-regulation reduces the antagonism of the bedaquiline-streptomycin combination. Retrospective analysis of 57 clinical trials of TB regimens using INDIGO-MTB revealed that synergistic combinations were significantly more efficacious than antagonistic combinations (p-value = 1 x 10-4) based on the percentage of patients with negative sputum cultures after 8 weeks of treatment. Our study establishes a framework for rapid assessment of TB drug combinations and is also applicable to other bacterial pathogens.\n\nIMPORTANCEMulti-drug combination therapy is an important strategy for treating tuberculosis, the worlds deadliest bacterial infection. Long treatment durations and growing rates of drug resistance have created an urgent need for new approaches to prioritize effective drug regimens. Hence, we developed a computational model called INDIGO-MTB, which identifies synergistic drug regimens from an immense set of possible drug combinations using pathogen response transcriptome elicited by individual drugs. Although the underlying input data for INDIGO-MTB was generated under in vitro broth culture conditions, the predictions from INDIGO-MTB correlated significantly with in vivo drug regimen efficacy from clinical trials. INDIGO-MTB also identified the transcription factor Rv1353c as a regulator of multiple drug interaction outcomes, which could be targeted for rationally enhancing drug synergy.

systems biology↗

bioRxiv2021

Mechanism Driven Early Stage Identification and Avoidance of Antisense Oligonucleotides Causing TRL9 Mediated Inflammatory Responses in Bjab cells

Nucleic acid-based PS-ASOs have the potential to activate cellular innate immune responses, and the level of activation can vary quite dramatically with sequence. Minimizing degree of proinflammatory effect is one of the main selection criteria for compounds intended to move into clinical trials. While a recently developed hPBMC-based assay showed excellent ability to detect innate immune active PS-ASOs, which can then be discarded from the developmental process, this assay is highly resource-intensive and easily affected by subject variability. This compelled us to develop of a more convenient high-throughput assay. Here, we describe a new in vitro assay, utilizing a cultured human Bjab cell line, which was developed and validated to identify PS-ASOs that may cause innate immune activation. The assay was calibrated to replicate results from the hPMBC assay. The Bjab assay was designed to be high throughput and more convenient by using RT-qPCR readout of mRNA of the chemokine Ccl22. The Bjab assay was also shown to be highly reproducible and to provide a large dynamic range in determining the immune potential of PS-ASOs via comparison to known benchmark PS-ASO controls that were previously shown to be either safe or inflammatory in clinical trials. In addition, we demonstrate that Bjab cells can be used to provide mechanistic information on PS-ASO-TLR9 dependent innate immune activation.

pharmacology and toxicology↗

bioRxiv2025

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy

Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlikely to suffice due to tumor antigen heterogeneity and off-tumor toxicities. To overcome these obstacles, we developed LogiCAR designer, a computational approach that utilizes single-cell transcriptomics data from patient tumors to systematically identify the cancer-specific antigen circuits with logic gates ("AND," "OR," and "NOT") that target the majority of cancer cells in a tumor while sparing normal cells and tissues as much as possible. LogiCAR designer efficiently scales to higher-order antigen combinations involving up to five genes. Applied to a large-scale dataset encompassing approximately 2 million cells (including > 620k tumor cells) from 342 clinical patient samples across all major breast cancer subtypes, LogiCAR designer identified antigen circuits with enhanced tumor-targeting efficacy and improved safety profiles compared to both previously reported circuits and single-target therapies in clinical trials. However, even these optimized shared circuits still proved insufficient for some patients. We hence systematically studied LogiCAR designers ability to identify highly effective CAR circuits that are individualized to each patient. Remarkably, such personalized CAR circuits provide estimated tumor-targeting efficacy tantamount to complete response in 76% of patients and partial response for all patients. Taken together, this analysis is the first systematic quantification of the efficacy and safety of all possible CAR circuits, showing that: (a) the quality of existing solutions leaves much to be desired; (b) the ability of shared circuits optimized across many patients is moderate, and finally, (c) individually tailored circuits offer significantly higher tumor-targeting efficacies for patients. LogiCAR designer offers a rigorous, data-driven way to facilitate the rational design of safe and effective CAR-based immunotherapies for cancer. Statement of Significance Development of a computational approach that efficiently identifies logic-gated CAR target combinations, called circuits, from single-cell transcriptomics, addressing a critical unmet clinical need. Application to the largest ensemble of breast cancer datasets to date, comprising [~]2 million cells (> 620k tumor cells) from 17 clinical cohorts, to identify CAR circuits predicted to be effective. Comprehensive safety profiling of candidate circuits spanning major tissues at both RNA and protein levels. Logic-gated CAR circuits generated by our pipeline address tumor heterogeneity and achieve efficacy and safety scores that surpass clinical trial and previously computationally identified circuits. Individualized rational CAR design offers a transformative approach to deliver precision-engineered CAR therapies with unprecedented efficacy.

bioinformatics↗

bioRxiv2026

Predicting resistance to synthetic lethal therapies in cancer

Background Synthetic lethality (SL) occurs when an alteration in one gene renders cells vulnerable to inhibition of another, offering a strategy for targeted cancer therapy. PARP inhibitors represent the first clinically approved synthetic lethal therapy, and numerous additional candidates are now in clinical trials. Unfortunately, resistance to these therapies is common and impairs their overall effectiveness. Although there is a growing understanding of the factors that predict synthetic lethal interactions, the prediction of mechanisms driving resistance to these effects remains largely unstudied. Here, we sought to determine whether SL resistance could be predicted a priori using protein-protein interaction networks integrated with additional molecular profiles. Methods We curated genome-wide CRISPR-Cas9 chemogenetic resistance screens spanning ten unique SL biomarker-target pairs. For each pair, we extracted resistance genes and computed features capturing protein-protein interactions, gene expression, and essentiality. We compared these features between resistance and non-resistance genes and developed a random forest classifier to predict resistance. Model performance was assessed using random-split and structured leave-one-out schemes based on biomarkers, targets, and SL pairs to ensure robust generalisation. Independent validation of the model was performed by assessing its ability to predict resistance to two SL therapies currently in clinical trials, MTAP-PRMT5 and ATM-ATR. Results Resistance genes were significantly enriched among the protein-protein interaction partners of SL pairs across multiple datasets, suggesting functional interaction networks can aid the prioritisation of resistance mechanisms. Resistance genes tended to be more co-expressed with their associated target and also displayed distinct essentiality patterns. Although no individual feature was strongly predictive, integrating all features in a random forest model improved discrimination between resistance and non-resistance genes. Under leave-one-SL-pair-out cross-validation - our most stringent test of generalisation - the model achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.72 across ten pairs (range 0.57-0.91; mean average precision ~9%); a random train-test split gave a higher AUROC (~0.78) that we treat as an optimistic upper bound, since biomarkers and targets are shared across folds. Average precision remained low in absolute terms, reflecting the inherent challenge of predicting extremely rare resistance events (<1% of genes). Conclusions Resistance to synthetic lethal therapies arises, at least in part, from predictable patterns of network connectivity, transcriptional coordination, and context-dependent essentiality. Our integrative framework provides a computational approach for anticipating resistance mechanisms across the expanding landscape of synthetic lethal therapies, offering a foundation for designing more durable and effective combination treatments.

cancer biology↗

bioRxiv2021

mGluR5 Negative Modulators for Fragile X: Resistance and Persistence

Fragile X syndrome (FXS) is caused by silencing of the human FMR1 gene and is the leading monogenic cause of intellectual disability and autism. Abundant preclinical data indicated that negative allosteric modulators (NAMs) of metabotropic glutamate receptor 5 (mGluR5) might be efficacious in treating FXS in humans. Initial attempts to translate these findings in clinical trials have failed, but these failures provide the opportunity for new discoveries that will improve future trials. The emergence of acquired treatment resistance ("tolerance") after chronic administration of mGluR5 NAMs is a potential factor in the lack of success. Here we confirm that FXS model mice display acquired treatment resistance after chronic treatment with the mGluR5 NAM CTEP in three assays commonly examined in the mouse model of FXS: (1) audiogenic seizure susceptibility, (2) sensory cortex hyperexcitability, and (3) hippocampal protein synthesis. Cross-tolerance experiments suggest that the mechanism of treatment resistance likely occurs at signaling nodes downstream of glycogen synthase kinase 3 (GSK3), but upstream of protein synthesis. The rapid emergence of tolerance to CTEP begs the question of how previous studies showed an improvement in inhibitory avoidance (IA) cognitive performance after chronic treatment. We show here that this observation was likely explained by timely inhibition of mGluR5 during a critical period, as brief CTEP treatment in juvenile mice is sufficient to provide a persistent improvement of IA behavior measured many weeks later. These data will be important to consider when designing future fragile X clinical trials using compounds that target the mGluR5-to-protein synthesis signaling cascade.

neuroscience↗

bioRxiv2021

Early-Stage Identification and Avoidance of Antisense Oligonucleotides Causing Species-Specific Inflammatory Responses in Human Volunteers Peripheral Blood Mononuclear Cells

A human peripheral blood mononuclear cell (PBMC)-based assay was developed to identify antisense oligonucleotide (ASO) with the potential to activate a cellular innate immune response outside of an acceptable level. The development of this assay was initiated when ISIS 353512 targeting the mRNA for human C-reactive protein was tested in a phase I clinical trial in which healthy human volunteers unexpectedly experienced increases in interleukin-6 (IL-6) and C-reactive protein. This level of immune stimulation was not anticipated following rodent and non-human primate safety studies in which no evidence of exaggerated proinflammatory effects were observed. The IL-6 increase induced by ISIS 353512 was caused by activation of B-cells. The IL-6 induction was inhibited by chloroquine pretreatment of the PBMC and the nature of the ASOs suggested that the response is mediated by a toll like receptor, in all likelihood TLR9. While assessing the inter PBMC donor variability, two class of responders to ISIS 353512 were identified (discriminator and non-discriminators). The discriminator source of PBMC was shown to produce low level of IL-6 after 24 hours in culture in absence of ASO treatment. The PBMC assay using discriminator donors was shown to be reproducible allowing to assess reliably the immune potential of ASOs via comparison to known benchmark ASO controls that were previously shown to be either safe or inflammatory in clinical trials.

pharmacology and toxicology↗

bioRxiv2018

Repurposing of drugs as novel influenza inhibitors from clinical gene expression infection signatures

BackgroundInfluenza virus infections remain a major and recurrent public health burden. The intrinsic ever-evolving nature of this virus, the suboptimal efficacy of current influenza inactivated vaccines, as well as the emergence of resistance against a limited antiviral arsenal, highlight the critical need for novel therapeutic approaches. In this context, the aim of this study was to develop and validate an innovative strategy for drug repurposing as host-targeted inhibitors of influenza viruses and the rapid evaluation of the most promising candidates in Phase II clinical trials.\n\nMethodsWe exploited in vivo global transcriptomic signatures of infection directly obtained from a patient cohort to determine a shortlist of already marketed drugs with newly identified, host-targeted inhibitory properties against influenza virus. The antiviral potential of selected repurposing candidates was further evaluated in vitro, in vivo and ex vivo.\n\nResultsOur strategy allowed the selection of a shortlist of 35 high potential candidates out of a rationalized computational screening of 1,309 FDA-approved bioactive molecules, 31 of which were validated for their significant in vitro antiviral activity. Our in vivo and ex vivo results highlight diltiazem, a calcium channel blocker currently used in the treatment of hypertension, as a promising option for the treatment of influenza infections. Additionally, transcriptomic signature analysis further revealed the so far undescribed capacity of diltiazem to modulate the expression of specific genes related to the host antiviral response and cholesterol metabolism. Finally, combination treatment with diltiazem and virus-targeted oseltamivir neuraminidase inhibitor further increased antiviral efficacy, prompting rapid authorization for the initiation of a Phase II clinical trial.\n\nConclusionsThis original, host-targeted, drug repurposing strategy constitutes an effective and highly reactive process for the rapid identification of novel anti-infectious drugs, with potential major implications for the management of antimicrobial resistance and the rapid response to future epidemic or pandemic (re)emerging diseases for which we are still disarmed.

microbiology↗