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bioRxiv2023

Simulating clinical trials for model-informed precision dosing: Using warfarin treatment as a use case

Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. neural network regression; 2. deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74 %. In comparison, the regression model and the deep RL model have success rates of 47.9% and 65.8 %, and median TTRs of 45 % and 68 %. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.

pharmacology and toxicology↗

bioRxiv2016

A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

Systemic sclerosis (SSc) is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of SSc patients treated with five therapies: mycophenolate mofetil (MMF), rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% OR -5 modified Rodnan Skin Score was applied to each study. We developed a machine learning approach that captured features beyond differential expression that was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab non-improvers were downregulated in MMF improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue-specificity and sensitivity of differential expression results.

genomics↗

bioRxiv2018

How cancer patients perceive clinical trials (CTs) in the era of CTs: Current perceptional status and differences between common and rare cancers

BackgroundAs perception have been recently highlighted as critical determinants of clinical trials (CTs) in cancer patients, we evaluated current perceptional status of CTs in cancer patients, focusing on differences between common and rare cancers.\n\nMaterials & MethodsFrom November 2015 to May 2017, we prospectively surveyed patients who have received anti-cancer treatment at Asan Medical Center using the PARTAKE questionnaires.\n\nResultsAmong 333 respondents, 70.9% and 29.1% had common and rare cancers, respectively. While 87.7% and 75.3% of patients answered that they heard of and knew about CTs, willingness to participate in CTs was expressed only in approximately 56% of patients although willingness was significantly correlated with awareness and perception. Surprisingly, patients with rare cancers showed significantly lower levels of awareness and perception (64.2% vs 79.9%, p=0.003 and 77.3% vs 91.9%, p<0.001), and consequently less willingness (47.4% vs 58.9%, p=0.06) compared to patients with common cancers. In addition, cancer patients still harbored fear with concerns about safety and reward, and substantial ignorance and mistrust about voluntariness of CTs, which was more predominant in patients with rare cancers.\n\nConclusionsPresent study identified relatively less willingness of CTs in cancer patients compared with generally favorable perception, and highlighted relative perceptional poverty in patients with rare cancers than those with common cancers. Further education and encouragement by research and public entities seem essential to raise motivation of CTs in cancer patients beyond good perception, especially for the patients with rare cancers.

epidemiology↗

bioRxiv2019

Kinetic fingerprint of antibody therapies predicts outcomes of Alzheimer clinical trials

The amyloid cascade hypothesis, according to which the self-assembly of amyloid-{beta} peptide (A{beta}) is a causative process in Alzheimers disease, has driven many therapeutic efforts for the past 20 years. Failures of clinical trials investigating A{beta}-targeted therapies have been interpreted as evidence against this hypothesis, irrespective of the characteristics and mechanisms of action of the therapeutic agents, which are highly challenging to assess. We bring together kinetic analysis with quantitative binding measurements to address the mechanisms of action of four clinical stage anti-A{beta} antibodies, aducanumab, gantenerumab, bapineuzumab and solanezumab. We reveal and quantify the striking differences of these antibodies on the aggregation kinetics and on the production of oligomeric aggregates, and link these effects to the affinity and stoichiometry of each antibody for monomeric and fibrillar forms of A{beta}. Our results uncover that, uniquely amongst these four antibodies, aducanumab dramatically reduces the flux of oligomeric forms of A{beta}.

biophysics↗

bioRxiv2026

Genomic epidemiology of Neisseria gonorrhoeae isolates from the G-ToG clinical trial, 2014-2016

Neisseria gonorrhoeae remains a major public health threat due to rising incidence and increasing antimicrobial resistance (AMR). To characterise the landscape of gonococci circulating in England a decade ago, we analysed whole genome sequences and clinical metadata from 354 isolates collected during the G-ToG clinical trial between 2014 and 2016. Comparison with global datasets demonstrated that the G-ToG isolates captured the breadth of UK gonococcal diversity, mapping across multiple lineages without evidence of marked bias related to participant demographic characteristics. The phylogenetic structure of gonococcal isolates within the collection was strongly shaped by sexual network, with statistically significant clustering of isolates circulating within heterosexual individuals compared with the men who have sex with men (MSM) transmission group. MSM-associated lineages exhibited higher minimum inhibitory concentration (MICs) to azithromycin and ceftriaxone and were enriched for corresponding chromosomal AMR determinants and AMR-associated plasmids. These data provide a valuable baseline preceding the implementation of several major UK public health interventions, including HIV pre-exposure prophylaxis (HIV PrEP), doxycycline post-exposure prophylaxis (Doxy-PEP) and vaccination with Bexsero. Continued integration of genomic and epidemiological surveillance will be essential to monitor whether these interventions reshape gonococcal transmission dynamics and AMR trajectories, guiding future control strategies.

genomics↗

bioRxiv2023

Tumor-agnostic transcriptome-based classifier identifies spatial infiltration patterns of CD8+ T cells in the tumor microenvironment and predicts clinical outcome in early- and late-phase clinical trials

BackgroundThe immune status of a patients tumor microenvironment (TME) may guide therapeutic interventions with cancer immunotherapy and help identify potential resistance mechanisms. Currently, patients immune status is mostly classified based on CD8+ tumor-infiltrating lymphocytes. An unmet need exists for comparable and reliable precision immunophenotyping tools that would facilitate clinical treatment-relevant decision-making and the understanding of how to overcome resistance mechanisms. MethodsWe systematically analyzed the CD8 immunophenotype of 2023 patients from 14 phase I-III clinical trials using immunohistochemistry (IHC) and additionally profiled gene expression by RNA-sequencing (RNA-seq). CD8 immunophenotypes were classified by pathologists into CD8-desert, CD8-excluded or CD8-inflamed tumors using CD8 IHC staining in epithelial and stromal areas of the tumor. Using regularized logistic regression, we developed an RNA-seq-based classifier as a surrogate to the IHC-based spatial classification of CD8+ tumor-infiltrating lymphocytes in the TME. ResultsThe CD8 immunophenotype and associated gene expression patterns varied across indications as well as across primary and metastatic lesions. Melanoma and kidney cancers were among the strongest inflamed indications, while CD8-desert phenotypes were most abundant in liver metastases across all tumor types. A good correspondence between the transcriptome and the IHC-based evaluation enabled us to develop a 92-gene classifier that accurately predicted the IHC-based CD8 immunophenotype in primary and metastatic samples (area under the curve (AUC) inflamed = 0.846; excluded = 0.712; desert = 0.855). The newly developed classifier was prognostic in The Cancer Genome Atlas (TCGA) data and predictive in lung cancer: patients with predicted CD8-inflamed tumors showed prolonged overall survival (OS) versus patients with CD8-desert tumors (hazard ratio [HR] 0.88; 95% confidence interval [CI]: 0.80-0.97) across TCGA, and longer OS upon immune checkpoint inhibitor administration (phase III OAK study) in non-small-cell lung cancer (HR 0.75; 95% CI: 0.58-0.97). ConclusionsWe provide a new precision immunophenotyping tool based on gene expression that reflects the spatial infiltration patterns of CD8+ lymphocytes in tumors. The classifier enables multiplex analyses and is easy to apply for retrospective, reverse translation approaches as well as for prospective patient enrichment to optimize the response to cancer immunotherapy. HIGHLIGHTSO_ST_ABSWhat is already known on this topicC_ST_ABST-cell infiltration, most commonly classified based on CD8+ T cell immunohistochemistry (IHC) staining, and various tumor microenvironment (TME)-specific resistance mechanisms, can impact response rates to cancer immunotherapy. What this study addsOur data provide new insights into the impact of tumor excision location and indication on the immune composition of the TME. We developed a transcriptome-based classifier that could accurately predict different spatial CD8+ T-cell infiltration patterns in the TME. We demonstrate the prognostic and predictive value of the classifier across independent patient cohorts (phase I to phase III trials). How this study might affect research, practice or policyOur new RNA-based tool provides a surrogate read-out for spatial IHC-based CD8 infiltration patterns, is easy to use and broadly applicable for both retrospective and prospective patient enrichment to enhance the effectiveness of cancer immunotherapy.

cancer biology↗

bioRxiv2018

Real Time PCR for the Evaluation of Treatment Response in Clinical Trials of Adult Chronic Chagas Disease: Usefulness of Serial Blood Sampling and qPCR Replicates.

This work evaluated a serial blood sampling procedure to enhance the sensitivity of duplex real time PCR (qPCR) for baseline detection and quantification of parasitic loads and post-treatment identification of failure in the context of clinical trials for treatment of chronic Chagas disease, namely DNDi-CH-E1224-001 (NCT01489228) and MSF-DNDi PCR sampling optimization study (NCT01678599). Patients from Cochabamba (N= 294), Tarija (N = 257), and Aiquile (N= 220) were enrolled. Three serial blood samples were collected at each time-point, and qPCR triplicates were tested per sample. The first two samples were collected during the same day and the third one seven days later.\n\nA patient was considered PCR positive if at least one qPCR replicate was detectable. Cumulative results of multiple samples and qPCR replicates enhanced the proportion of pre-treatment sample positivity from 54.8 to 76.2%, 59.5 to 77.8%, and 73.5 to 90.2% in Cochabamba, Tarija, and Aiquile cohorts, respectively and increased cumulative detection of treatment failure from 72.9 to 91.7%, 77.8 to 88.9%, and 42.9 to 69.1% for E1224 low, short, and high dosage regimes, respectively; and from 4.6 to 15.9% and 9.5 to 32.1% for the benznidazole (BZN) arm in the DNDi-CH-E1224-001 and MSF-DNDi studies, respectively. The monitoring of patients treated with placebo in the DNDi-CH-E1224-001 trial revealed fluctuations in parasitic loads and occasional non-detectable results. This serial sampling strategy enhanced PCR sensitivity to detecting treatment failure during follow-up and has the potential for improving recruitment capacity in Chagas disease trials which require an initial positive qPCR result for patient admission.

microbiology↗

bioRxiv2024

Predictive Immunoinformatics Reveal Promising Safety and Anti-Onchocerciasis Protective Immune Response Profiles to Vaccine Candidates (Ov-RAL-2 and Ov-103) in Anticipation of Phase I Clinical Trials

Onchocerciasis is a devastating tropical disease that causes severe eye and skin lesions. As global efforts shift from disease control to elimination, prophylactic/therapeutic vaccines have emerged as alternative elimination tools. Notably, Ov-RAL-2 and Ov-103 antigens have shown great promise in preclinical studies and plans are underway for clinical trials. Here, we predict the immunogenicity and other vaccine-related parameters for both antigens using immunoinformatics, as potential vaccine candidates against onchocerciasis. The analysis reveals that both antigens exhibit a favourable safety profile, making them promising candidates poised for human trials. Importantly, in silico immune simulation forecasts heightened antibody production and sustained cellular responses for both vaccine candidates. Indeed, the antigens were predicted to harbour substantial numbers of a wide range of distinct epitopes associated with protective responses against onchocerciasis, as well as the potential for stimulating innate immune TLR-4 receptor recognition with Ov-103 exhibiting better structural efficiency and antigenicity with no homology to human proteins compared to Ov-RAL-2. Overall, we provide herein valuable insights for advancing the development of Ov-103 and RAL-2 vaccine candidates against onchocerciasis in humans. Authors summaryTo address the significant impact of onchocerciasis, a tropical disease commonly known as river blindness, we have employed computational tools to assess the viability of two promising vaccine candidates, namely Ov-RAL-2 and Ov-103. Existing control strategies alone are insufficient to eliminate the disease. Our study utilises advanced immunoinformatics techniques to systematically evaluate the safety, antigenicity, and immunogenic properties of these antigens as potential vaccine candidates against onchocerciasis prior to human trials. Our analysis revealed that both vaccine candidates demonstrate favourable safety profiles and possess the capability to induce robust antibody responses and cellular immunity. Notably, we identified numerous distinct epitopes present within each vaccine candidate that are associated with protective immunity against onchocerciasis. The abundance of these epitopes suggests that both vaccine candidates have the potential to activate the immune system through diverse humoral and cellular response mechanisms. By providing these valuable insights, our research assists in guiding the development of Ov-103 and Ov-RAL-2 as effective vaccines against onchocerciasis. Ultimately, our findings contribute to the global endeavour to eliminate this debilitating disease and enhance the quality of life for the millions of affected individuals.

bioinformatics↗

bioRxiv2026

Modeling quantifies in vivo neutralization, Fc-mediated killing, and resistance in human clinical trials of five anti-HIV broadly neutralizing antibodies

Broadly neutralizing antibodies (bnAbs) are a promising intervention for HIV prevention, therapy, and cure. bnAb optimization requires precise quantification of in vivo functions, many of which cannot be directly measured in humans. We therefore performed a mathematical modeling meta-analysis which integrated four clinical trials and reproduced serial bnAb concentrations, viral loads, and bnAb sensitivities (IC50) in 43 viremic trial participants who received an infusion of VRC01, VRC01LS, VRC07-523LS, 3BNC117 or 10-1074. We compared >300 mathematical models for their ability to recapitulate multi-strain HIV dynamics following bnAb infusion. For each bnAb, our best model identified a scaling factor of 36-462 to project in vivo activity from in vitro IC50, quantified Fc-mediated infected cell killing in humans over time, and projected the timing of bnAb-resistant strain emergence. Using this holistic profile, VRC07-523-LS was generally optimal.

immunology↗

bioRxiv2022

TRACKING HEMATOPOIETIC STEM CELL EVOLUTION IN A WISKOTT-ALDRICH CLINICAL TRIAL

Hematopoietic Stem Cells (HSC) are the cells that give rise to all other blood cells and, as such, they are crucial in the healthy development of individuals. Wiskott-Aldrich Syndrome (WAS) is a severe disorder affecting the regulation of hematopoietic cells and is caused by mutations in the WASP gene. We consider data from a revolutionary gene therapy clinical trial, where HSC harvested from 3 WAS patients bone marrow have been edited and corrected using viral vectors. Upon re-infusion into the patient, the HSC multiply and differentiate into other cell types. The aim is to unravel the cell multiplication and cell differentiation process, which has until now remained elusive. This paper models the replenishment of blood lineages resulting from corrected HSC via a multivariate, density-dependent Markov process and develops an inferential procedure to estimate the dynamic parameters given a set of temporally sparsely observed trajectories. Starting from the master equation, we derive a system of non-linear differential equations for the evolution of the first- and second-order moments over time. We use these moment equations in a generalized method-of-moments framework to perform inference. The performance of our proposal has been evaluated by considering different sampling scenarios and measurement errors of various strengths using a simulation study. We also compared it to another state-of-the-art approach and found that our method is statistically more efficient. By applying our method to the Wiskott-Aldrich Syndrome gene therapy data we found strong evidence for a myeloid-based developmental pathway of hematopoietic cells where fates of lymphoid and myeloid cells remain coupled even after the loss of erythroid potential. All code used in this manuscript can be found in the online Supplement, and the latest version of the code is available at github. com/dp3ll1n/SLCDP_v1.0.

bioinformatics↗

bioRxiv2025

Pixel intensity of wing photos used to predict age of Anopheles gambiae caught during the RIMDAMAL II clinical trial

Mosquito age-grading is important for evaluating mosquito control efforts and estimating pathogen transmission risk. We previously developed a simple, low-cost, and high-throughput method to age-grade mosquitoes by computing the pixel intensity (PI) of wing photos, which reflects wing scale loss over time. Here the technique was refined and used to understand wild Anopheles gambiae population structures from the RIMDAMAL II clinical trial. Wing photos from lab-reared An. gambiae had narrower PI ranges compared to wild An. gambiae s.l., but the PI distributions reflected wild population structures where most have lower PI values and very few high PI values. A model was then fitted to samples from lab mosquitoes of known ages and used to interpolate unknown ages from the wild populations (median age of 4.96 [0.8-14.93] days old). Analyses from the RIMDAMAL II trial indicate that while ivermectin mass drug administrations in the intervention arm may have modestly influenced PI and predicted age relative to controls, distributions of new dual-chemistry bed nets in both arms were associated with a strong effect on PI and predicted age structure. These data suggest this method can be used to rapidly age-grade wild mosquito populations and infer the efficacy of vector control interventions.

microbiology↗

bioRxiv2020

Systematic analysis of protein targets associated with adverse events of drugs from clinical trials and post-marketing reports

Adverse drug reactions (ADRs) are undesired effects of medicines that can harm patients and are a significant source of attrition in drug development. ADRs are anticipated by routinely screening drugs against secondary pharmacology protein panels. However, there is still a lack of quantitative information on the links between these off-target proteins and the risk of ADRs in humans. Here, we present a systematic analysis of associations between measured and predicted in vitro bioactivities of drugs, and adverse events (AEs) in humans from two sources of data: the Side Effect Resource (SIDER), derived from clinical trials, and the Food and Drug Administration Adverse Event Reporting System (FAERS), derived from post-marketing surveillance. The ratio of a drug’s in vitro potency against a given protein relative to its therapeutic unbound drug plasma concentration was used to select proteins most likely to be relevant to in vivo effects. In examining individual target bioactivities as predictors of AEs, we found a trade-off between the Positive Predictive Value and the fraction of drugs with AEs that can be detected, however considering sets of multiple targets for the same AE can help identify a greater fraction of AE-associated drugs. Of the 45 targets with statistically significant associations to AEs, 30 are included on existing safety target panels. The remaining 15 targets include 8 carbonic anhydrases, of which CA5B was significantly associated with cholestatic jaundice. We include the full quantitative data on associations between in vitro bioactivities and AEs in humans in this work, which can be used to make a more informed selection of safety profiling targets.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pharmacology and toxicology↗

bioRxiv2024

Generating virtual patient data for in-silico clinical trials of medical devices during extracorporeal membrane oxygenationtreatment

This study introduces a virtual patient generation model as online tool through the generation of high-quality synthetic data, addressing challenges like privacy cocerns and limited dataset sizes. Using a Conditional Tabular Generative Adversarial Network (CTGAN), we generated synthetic data from the Electronic Health Records (EHR) of 767 veno-venous extracorporeal membrane oxygenation (ECMO) patients, focusing on 55 critical therapy parameters. Rigorous preprocessing, imputation, and model tuning ensured that the synthetic data closely mirrored real patient records, achieving a 86.6% coverage score and minimal deviations in data correlations. The tool is integrated into a web platform - https://cve-sim.de/ecmo-vpg, allowing researchers to generate and visualize virtual patient cohorts, potentially enhancing ECMO research and reducing the need for extensive clinical trials.

bioinformatics↗

bioRxiv2018

Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects

Biomedical scientists face major challenges in developing novel drugs for unmet medical needs. Only a small fraction of early drug programs progress to the market, due to safety and efficacy failures, despite extensive efforts to predict drug and target safety as early as possible using a variety of assays in vitro and in preclinical species. In principle, characterizing the effect of natural variation in the genes encoding drug targets should present a powerful alternate approach to predict not only whether a protein will be an effective drug target, but also whether a protein will be an inherently safe drug target, while avoiding the challenges of translating biology from experiments in non-human species. We have embarked on a retrospective analysis, demonstrating for the first time a statistical link between the organ systems involved in genetic syndromes of drug target genes and the organ systems in which side effects are observed clinically. Across 1,819 drugs and 21 organ system phenotype categories analyzed, drug side effects are more likely to occur in organ systems where there is genetic evidence of a link between the drug target and a phenotype involving that organ system, compared to when there is no such genetic evidence (30.0% vs 19.2%; OR = 1.80). Conversely, we find that having genetic evidence of a Mendelian syndrome involving a drug target in which a certain organ system is unaffected decreases the likelihood that side effects will manifest in that organ system, relative to having no informative syndrome (18.5% vs 20.2%; OR = 0.89). We find a relationship between genetics and side effects even when controlling for known confounders such as drug delivery route and indication. We highlight examples where genetics of drug targets could have anticipated side effects observed during clinical trials. This result suggests that human genetic data should be routinely used to predict potential safety issues associated with novel drug targets. This may lead to selection of better targets, appropriate monitoring of putative side effects early in development, reduction of the use of preclinical animal experiments, and ultimately increased success of molecules. Furthermore, deeply phenotyping human knockouts will be critically important to understand the full spectrum of effects that a new drug may elicit.

genetics↗

bioRxiv2024

A virtual clinical trial of psychedelics to treat patients with disorders of consciousness

Disorders of consciousness (DoC), including the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), have limited treatment options. Recent research suggests that psychedelic drugs, known for their complexity-enhancing properties, could be promising treatments for DoC. This study uses whole-brain computational models to explore this potential. We created individualised models for DoC patients, optimised with empirical fMRI and diffusion-weighted imaging (DWI) data, and simulated the administration of LSD and psilocybin. We used an in-silico perturbation protocol to distinguish between different states of consciousness, including DoC, anaesthesia, and the psychedelic state, and assess the dynamical stability of the brains of DoC patients pre- and post-psychedelic simulation. Our findings indicate that LSD and psilocybin shift DoC patients brains closer to criticality, with a greater effect in MCS patients. In UWS patients, the treatment response correlates with structural connectivity, while in MCS patients, it aligns with baseline functional connectivity. This virtual clinical trial lays a computational foundation for using psychedelics in DoC treatment and highlights the future role of computational modelling in drug discovery and personalised medicine.

neuroscience↗

bioRxiv2021

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data. Unfortunately, measurements for individual CpGs can be surprisingly unreliable due to technical noise, and this may limit the utility of epigenetic clocks. We report that noise produces deviations up to 3 to 9 years between technical replicates for six major epigenetic clocks. The elimination of low-reliability CpGs does not ameliorate this issue. Here, we present a novel computational multi-step solution to address this noise, involving performing principal component analysis on the CpG-level data followed by biological age prediction using principal components as input. This method extracts shared systematic variation in DNAm while minimizing random noise from individual CpGs. Our novel principal-component versions of six clocks show agreement between most technical replicates within 0 to 1.5 years, equivalent or improved prediction of outcomes, and more stable trajectories in longitudinal studies and cell culture. This method entails only one additional step compared to traditional clocks, does not require prior knowledge of CpG reliabilities, and can improve the reliability of any existing or future epigenetic biomarker. The high reliability of principal component-based epigenetic clocks will make them particularly useful for applications in personalized medicine and clinical trials evaluating novel aging interventions.

bioinformatics↗

bioRxiv2018

An Assessment of Skin Lesion Measurement Techniques for Use in Clinical Trials of Acute Bacterial Skin and Skin Structure Infections

BackgroundLimited data are available to support a reproducible measurement technique that could be used to assess the response of a skin lesion associated with a bacterial infection to antibacterial therapy.\n\nMethodsThis multicenter, observational study enrolled patients with a major cutaneous abscess, a traumatic wound or surgical site infection, or a cellulitis. The primary objective was to characterize the intra- and inter-observer variability inherent in measuring the size of the erythema associated with the presenting skin infection. At least two observers made ruler measurements of the infection site with the length of the infection measured as the longest dimension of the erythematous area and width measured as the largest dimension perpendicular to the longest length. Intra- and inter-observer variability was determined by the intraclass correlation coefficient (ICC). Photographs, tracings, and thermal imaging were also performed.\n\nResultsThe intra-observer ICC (95% CI) for lesion area as measured by ruler was 0.999 (0.998, 0.999), suggesting only a very small amount of the variability was due to measurement error. The difference in mean lesion area measurements by the same observer was <1%. The inter-observer ICC (95% CI) for lesion area as measured by ruler was 0.990 (0.981, 0.995) suggesting that the results between observers were also highly reliable.\n\nConclusionsMeasurement of infection area as defined by erythema and measured by ruler shows excellent intra- and inter-observer reliability and can be used in future clinical trials of acute bacterial skin infections.

pathology↗

bioRxiv2015

Diagnostic Assessment of Osteosarcoma Chemoresistance Based on Virtual Clinical Trials

Osteosarcoma is the most common primary bone tumor in pediatric and young adult patients. Successful treatment of osteosarcomas requires a combination of surgical resection and systemic chemotherapy, both neoadjuvant (prior to surgery) and adjuvant (after surgery). The degree of necrosis following neoadjuvant chemotherapy correlates with the subsequent probability of disease-free survival. Tumors with less than 10% of viable cells after treatment represent patients with a more favorable prognosis. However, being able to predict early, such as at the time of the pre-treatment tumor biopsy, how the patient will respond to the standard chemotherapy would provide an opportunity for more personalized patient care. Patients with unfavorable predictions could be studied in a protocol, rather than a standard setting, towards improving therapeutic success. The onset of necrotic cells in osteosarcomas treated with chemotherapeutic agents is a measure of tumor sensitivity to the drugs. We hypothesize that the remaining viable cells, i.e., cells that have not responded to the treatment, are chemoresistant, and that the pathological characteristics of these chemoresistant tumor cells within the osteosarcoma pre-treatment biopsy can predict tumor response to the standard-of-care chemotherapeutic treatment. This hypothesis can be tested by comparing patient histopathology samples before, as well as after treatment to identify both morphological and immunochemical cellular features that are characteristic of chemoresistant cells, i.e., cells that survived treatment. Consequently, using computational simulations of dynamic changes in tumor pathology under the simulated standard of care chemotherapeutic treatment, one can couple the pre- and post-treatment morphological and spatial patterns of chemoresistant cells, and correlate them with patient clinical diagnoses. This procedure, that we named Virtual Clinical Trials, can serve as a potential predictive biomarker providing a novel value added decision support tool for oncologists.

Systems Biology↗