Improving population scale statistical phasing with whole-genome sequencing data.

Haplotype estimation, or phasing, has gained significant traction in large-scale projects due to its valuable contributions to population genetics, variant analysis, and the creation of reference panels for imputation and phasing of new samples. To scale with the growing number of samples, haplotype estimation methods designed for population scale rely on highly optimized statistical models to phase genotype data, and usually ignore read-level information. Statistical methods excel in resolving common variants, however, they still struggle at rare variants due to the lack of statistical information. In this study we introduce SAPPHIRE, a new method that leverages whole-genome sequencing data to enhance the precision of haplotype calls produced by statistical phasing. SAPPHIRE achieves this by refining haplotype estimates through the realignment of sequencing reads, particularly targeting low-confidence phase calls. Our findings demonstrate that SAPPHIRE significantly enhances the accuracy of haplotypes obtained from state of the art methods and also provides the subset of phase calls that are validated by sequencing reads. Finally, we show that our method scales to large data sets by its successful application to the extensive 3.6 Petabytes of sequencing data of the last UK Biobank 200,031 sample release.

Pharmacokinetic Consideration of Venetoclax in Acute Myeloid Leukemia Patients: A Potential Candidate for TDM? A Short Communication.

BACKGROUND: Venetoclax (VNX)-based regimens have demonstrated significantly favorable outcomes in patients with acute myeloid leukemia (AML) and are now becoming the standard treatment. Tyrosine kinase inhibitors are administered at a fixed dose, irrespective of body surface area or weight. For such orally targeted therapies, real-world data have highlighted a larger pharmacokinetic (PK) interindividual variability (IIV) than expected. Even if VNX PKs have been well characterized and described in the literature, only 1 clinical trial-based PK study has been conducted in patients with AML. This study aimed to evaluate the PK of VNX in AML patients. MATERIAL AND METHODS: We retrospectively analyzed all patients treated with a combination of VNX-azacitidine between January and July 2022 at our center, using at least 1 available VNX blood sample. Based on a previously published population PK model, individual PK parameters were estimated to evaluate the exposure and IIV. RESULTS: and Discussion. Twenty patients received VNX in combination with azacitidine, according to the PK data. A total of 93 plasma concentrations were collected. The dose of VNX was 400 mg, except in 7 patients who received concomitant posaconazole (VNX 70 mg). The patients' weight ranged from 49 kg to 108 kg (mean = 78 kg). Mean individual clearance was 13.5 ± 9.4 L/h with mean individual daily area under the concentration-time curves of 35.8 mg.h/L with significant IIV (coefficient of variation = 41.1%). Ten patients were still alive (8 in complete response), but all experienced at least 1 hematological toxicity of grade ≥ 3. CONCLUSIONS: Based on the observed large PK variability in the data from our real-world AML patients, the risk of drug interactions and the recommended fixed-dosage regimen of VNX therapeutic drug monitoring may be useful.

XSI-a genotype compression tool for compressive genomics in large biobanks.

MOTIVATION: Generation of genotype data has been growing exponentially over the last decade. With the large size of recent datasets comes a storage and computational burden with ever increasing costs. To reduce this burden, we propose XSI, a file format with reduced storage footprint that also allows computation on the compressed data and we show how this can improve future analyses. RESULTS: We show that xSqueezeIt (XSI) allows for a file size reduction of 4-20× compared with compressed BCF and demonstrate its potential for 'compressive genomics' on the UK Biobank whole-genome sequencing genotypes with 8× faster loading times, 5× faster run of homozygozity computation, 30× faster dot products computation and 280× faster allele counts. AVAILABILITY AND IMPLEMENTATION: The XSI file format specifications, API and command line tool are released under open-source (MIT) license and are available at https://github.com/rwk-unil/xSqueezeIt. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predictive Performance of Bayesian Vancomycin Monitoring in the Critically Ill.

OBJECTIVES: It is recommended that therapeutic monitoring of vancomycin should be guided by 24-hour area under the curve concentration. This can be done via Bayesian models in dose-optimization software. However, before these models can be incorporated into clinical practice in the critically ill, their predictive performance needs to be evaluated. This study assesses the predictive performance of Bayesian models for vancomycin in the critically ill. DESIGN: Retrospective cohort study. SETTING: Single-center ICU. PATIENTS: Data were obtained for all patients in the ICU between 1 January, and 31 May 2020, who received IV vancomycin. The predictive performance of three Bayesian models were evaluated based on their availability in commercially available software. Predictive performance was assessed via bias and precision. Bias was measured as the mean difference between observed and predicted vancomycin concentrations. Precision was measured as the sd of bias, root mean square error, and 95% limits of agreement based on Bland-Altman plots. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 466 concentrations from 188 patients were used to evaluate the three models. All models showed low bias (-1.7 to 1.8 mg/L), which was lower with a posteriori estimate (-0.7 to 1.8 mg/L). However, all three models showed low precision in terms of sd (4.7-8.8 mg/L) and root mean square error (4.8-8.9 mg/L). The models underpredicted at higher observed vancomycin concentrations (bias 0.7-3.2 mg/L for < 20 mg/L; -5.1 to -2.3 for ≥ 20 mg/L) and the Bland-Altman plots showed a great deviation between observed and predicted concentrations. CONCLUSIONS: Bayesian models of vancomycin show not only low bias, but also low precision in the critically ill. Thus, Bayesian-guided dosing of vancomycin in this population should be used cautiously.

Bayesian Forecasting for Intravenous Tobramycin Dosing in Adults With Cystic Fibrosis Using One Versus Two Serum Concentrations in a Dosing Interval.

BACKGROUND: Intravenous tobramycin treatment requires therapeutic drug monitoring (TDM) to ensure safety and efficacy when used for prolonged treatment, as in infective exacerbations of cystic fibrosis. The 24-hour area under the concentration-time curve (AUC24) is widely used to guide dosing; however, there remains variability in practice around methods for its estimation. The objective of this study was to determine the potential for a sparse-sampling strategy using a single postinfusion tobramycin concentration and Bayesian forecasting to assess the AUC24 in routine practice. METHODS: Adults with cystic fibrosis receiving once-daily tobramycin had paired concentrations measured 2 hours (c1) and 6 hours (c2) after the end of infusion as routine monitoring. AUC24 exposures were estimated using Tucuxi, a Bayesian forecasting application that incorporates a validated population pharmacokinetic model. Simulations were performed to estimate AUC24 using the full data set using c1 and c2, compared with estimates using depleted data sets (c1 or c2 only), with and without concentration data from earlier in the course. The agreement between each simulation condition and the reference was assessed graphically and numerically using the median difference (∆) AUC24 and (relative) root mean square error (rRMSE) as measures of bias and accuracy, respectively. RESULTS: A total of 55 patients contributed 512 concentrations from 95 tobramycin courses and 256 TDM episodes. Single concentration methods performed well, with median ∆AUC24 <2 mg·h·L-1 and rRMSE of <15% for sequential c1 and c2 conditions. CONCLUSIONS: Bayesian forecasting implemented in Tucuxi, using single postinfusion concentrations taken 2-6 hours after tobramycin administration, yield similar exposure estimates to more intensive (two-sample) methods and are suitable for routine TDM practice.

Bayesian Vancomycin Model Selection for Therapeutic Drug Monitoring in Neonates.

BACKGROUND AND OBJECTIVE: Pharmacokinetic models can inform drug dosing of vancomycin in neonates to optimize therapy. However, the model selected needs to describe the intended population to provide appropriate dose recommendations. Our study aims to identify the population pharmacokinetic (PopPK) model(s) with the best performance to predict vancomycin exposure in neonates in our hospital. METHODS: Relevant published PopPK models for vancomycin in neonates were selected based on demographics and vancomycin dosing strategy. The predictive performance of the models was evaluated in Tucuxi using a local cohort of 69 neonates. Mean absolute error (MAE), relative bias (rBias) and relative root mean square error (rRMSE) were used to quantify the accuracy and precision of the predictive performance of each model for three different approaches: a priori, a posteriori, and Bayesian forecasting for the next course of therapy based on the previous course predictions. A PopPK model was considered clinically acceptable if rBias was between ± 20 and 95% confidence intervals included zero. RESULTS: A total of 25 PopPK models were identified and nine were considered suitable for further evaluation. The model of De Cock et al. 2014 was the only clinically acceptable model based on a priori [MAE 0.35 mg/L, rBias 0.8 % (95% confidence interval (CI) - 7.5, 9.1%), and rRMSE 8.9%], a posteriori [MAE 0.037 mg/L, rBias - 0.23% (95% CI - 1.3, 0.88%), and rRMSE 6.02%] and Bayesian forecasting for the next courses [MAE 0.89 mg/L, rBias 5.45% (95% CI - 8.2, 19.1%), and rRMSE 38.3%) approaches. CONCLUSIONS: The De Cock model was selected based on a comprehensive approach of model selection to individualize vancomycin dosing in our neonates.

Exploiting parallelization in positional Burrows-Wheeler transform (PBWT) algorithms for efficient haplotype matching and compression.

Summary: The positional Burrows-Wheeler transform (PBWT) data structure allows for efficient haplotype data matching and compression. Its performance makes it a powerful tool for bioinformatics. However, existing algorithms do not exploit parallelism due to inner dependencies. We introduce a new method to break the dependencies and show how to fully exploit modern multi-core processors. Availability and implementation: Source code and applications are available at https://github.com/rwk-unil/parallel_pbwt. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Precision Oncology by Point-of-Care Therapeutic Drug Monitoring and Dosage Adjustment of Conventional Cytotoxic Chemotherapies: A Perspective.

Therapeutic drug monitoring (TDM) of conventional cytotoxic chemotherapies is strongly supported yet poorly implemented in daily practice in hospitals. Analytical methods for the quantification of cytotoxic drugs are instead widely presented in the scientific literature, while the use of these therapeutics is expected to keep going for longer. There are two main issues hindering the implementation of TDM: turnaround time, which is incompatible with the dosage profiles of these drugs, and exposure surrogate marker, namely total area under the curve (AUC). Therefore, this perspective article aims to define the adjustment needed from current to efficient TDM practice for cytotoxics, namely point-of-care (POC) TDM. For real-time dose adjustment, which is required for chemotherapies, such POC TDM is only achievable with analytical methods that match the sensitivity and selectivity of current methods, such as chromatography, as well as model-informed precision dosing platforms to assist the oncologist with dose fine-tuning based on quantification results and targeted intervals.

Therapeutic drug monitoring of cefepime in a non-critically ill population: retrospective assessment and potential role for model-based dosing.

Objectives: To describe the therapeutic drug monitoring (TDM) of cefepime in non-critically ill adults and compare four different ways of dosing: conventional table-based; empirically adjusted following TDM; individualized based on a population pharmacokinetic (PopPK) model without TDM; and TDM-adjusted with a Bayesian approach integrating TDM and PopPK. Methods: We conducted a retrospective study in a tertiary centre to examine the current practice of TDM and to evaluate the potential for improvement by PopPK-based software individualization. The prediction of trough concentrations and the total daily doses (TDD) prescribed according to each approach were compared by calculating the mean logarithmic bias and the root mean squared error, complemented by linear regression and variance analysis. Results: Among 168 trough concentrations in 119 patients (median: 12 mg/L), 38.6% of measurements exceeded 15 mg/L, the reported threshold for neurotoxicity. Nine patients developed neurotoxicity. The prediction performance of PopPK alone for trough concentrations was moderate, but clearly improved after integration of TDM. Accordingly, TDD were significantly lower for a priori PopPK-based dosage (mean: 2907 mg/24 h) compared with actual table-based dosage (4625 mg/24 h, P < 0.001). They were also lower for a posteriori dosage based on PopPK and TDM (3377 mg/24 h) compared with actual dosage after empirical TDM (4233 mg/24 h, P < 0.001), as model-based adjustment privileged more frequent administrations. Conclusions: Our observations support routine TDM of cefepime to prevent overdosing and subsequent toxicity in the non-critically ill. Software-based individualization seems promising to optimize the benefits of TDM, but has little potential to replace it.

Discrepancies Between Bayesian Vancomycin Models Can Affect Clinical Decisions in the Critically Ill.

Purpose: To assess the agreement in 24-hour area under the curve (AUC24) value estimates between commonly used vancomycin population pharmacokinetic models in the critically ill. Materials and Methods: Adults admitted to intensive care who received intravenous vancomycin and had a serum vancomycin concentration available were included. AUC24 values were determined using Tucuxi (revision cd7bd7a8) for dosing intervals with a vancomycin concentration using three models (Goti 2018, Colin 2019, and Thomson 2009) previously evaluated in the critically ill. AUC24 values were categorized as subtherapeutic (<400 mg·h/L), therapeutic (400-600 mg·h/L), or toxic (>600 mg·h/L), assuming a minimum inhibitory concentration of 1 mg/L. AUC24 value categorization was compared across the three models and reported as percent agreement. Results: Overall, 466 AUC24 values were estimated in 188 patients. Overall, 52%, 42%, and 47% of the AUC24 values were therapeutic for the Goti, Colin, and Thomson models, respectively. The agreement of AUC24 values between all three models was 48% (223/466), Goti-Colin 59% (193/466), Goti-Thomson 68% (318/466), and Colin-Thomson 67% (314/466). Conclusion: In critically ill patients, vancomycin AUC24 values obtained from different pharmacokinetic models are often discordant, potentially contributing to differences in dosing decisions. This highlights the importance of selecting the optimal model.

Implementation and Cross-Validation of a Pharmacokinetic Model for Precision Dosing of Busulfan in Hematopoietic Stem Cell Transplanted Children.

Busulfan, a drug used in conditioning prior to hematopoietic stem cell transplantation (HSCT) in children, has a narrow therapeutic margin. The model-informed precision dosing (MIPD) of busulfan is desirable, but there is a lack of validated tools. The objective of this study was to implement and cross-validate a population pharmacokinetic (PK) model in the Tucuxi software for busulfan MIPD in HSCT children. A search of the literature was performed to identify candidate population PK models. The goodness of fit of three selected models was assessed in a dataset of 178 children by computing the mean error (ME) and root-mean-squared error of prediction (RMSE). The best model was implemented in Tucuxi. The individual predicted concentrations, the area under the concentration-time curve (AUC), and dosage requirements were compared between the Tucuxi model and a reference model available in the BestDose software in a subset of 61 children. The model from Paci et al. best fitted the data in the full dataset. In a subset of 61 patients, the predictive performance of Tucuxi and BestDose models was comparable with ME values of 6.4% and -2.5% and RMSE values of 11.4% and 13.6%, respectively. The agreement between the estimated AUC and the predicted dose was good, with 6.6% and 4.9% of the values being out of the 95% limits of agreement, respectively. To conclude, a PK model for busulfan MIPD was cross-validated and is now available in the Tucuxi software.

Implementation and Comparison of Two Pharmacometric Tools for Model-Based Therapeutic Drug Monitoring and Precision Dosing of Daptomycin.

Daptomycin is a candidate for therapeutic drug monitoring (TDM). The objectives of this work were to implement and compare two pharmacometric tools for daptomycin TDM and precision dosing. A nonparametric population PK model developed from patients with bone and joint infection was implemented into the BestDose software. A published parametric model was imported into Tucuxi. We compared the performance of the two models in a validation dataset based on mean error (ME) and mean absolute percent error (MAPE) of individual predictions, estimated exposure and predicted doses necessary to achieve daptomycin efficacy and safety PK/PD targets. The BestDose model described the data very well in the learning dataset. In the validation dataset (94 patients, 264 concentrations), 21.3% of patients were underexposed (AUC24h < 666 mg.h/L) and 31.9% of patients were overexposed (Cmin > 24.3 mg/L) on the first TDM occasion. The BestDose model performed slightly better than the model in Tucuxi (ME = -0.13 ± 5.16 vs. -1.90 ± 6.99 mg/L, p < 0.001), but overall results were in agreement between the two models. A significant proportion of patients exhibited underexposure or overexposure to daptomycin after the initial dosage, which supports TDM. The two models may be useful for model-informed precision dosing.

SpikeOnChip : A Custom Embedded Platform for Neuronal Activity Recording and Analysis.

In this paper we present SpikeOnChip, a custom embedded platform for neuronal activity recording and online analysis. The SpikeOnChip platform was developed in the context of automated drug testing and toxicology assessments on neural tissue made from human induced pluripotent stem cells. The system was developed with the following goals: to be small, autonomous and low power, to handle micro-electrode arrays with up to 256 electrodes, to reduce the amount of data generated from the recording, to be able to do computation during acquisition, and to be customizable. This led to the choice of a Field Programmable Gate Array System-On-Chip platform. This paper focuses on the embedded system for acquisition and processing with key features being the ability to record electrophysiological signals from multiple electrodes, detect biological activity on all channels online for recording, and do frequency domain spectral energy analysis online on all channels during acquisition. Development methodologies are also presented. The platform is finally illustrated in a concrete experiment with bicuculline being administered to grown human neural tissue through microfluidics, resulting in measurable effects in the spike recordings and activity. The presented platform provides a valuable new experimental instrument that can be further extended thanks to the programmable hardware and software.

The Steps to Therapeutic Drug Monitoring: A Structured Approach Illustrated With Imatinib.

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

Continuous high speed coherent one-way quantum key distribution.

Quantum key distribution (QKD) is the first commercial quantum technology operating at the level of single quanta and is a leading light for quantum-enabled photonic technologies. However, controlling these quantum optical systems in real world environments presents significant challenges. For the first time, we have brought together three key concepts for future QKD systems: a simple high-speed protocol; high performance detection; and integration both, at the component level and for standard fibre network connectivity. The QKD system is capable of continuous and autonomous operation, generating secret keys in real time. Laboratory and field tests were performed and comparisons made with robust InGaAs avalanche photodiodes and superconducting detectors. We report the first real world implementation of a fully functional QKD system over a 43 dB-loss (150 km) transmission line in the Swisscom fibre optic network where we obtained average real-time distribution rates over 3 hours of 2.5 bps.

POEtic: an electronic tissue for bio-inspired cellular applications.

In this paper, we introduce the general architecture of a new electronic tissue called POEtic. This reconfigurable circuit is designed to ease the implementation of bio-inspired systems that bring cellular applications into play. It contains special features that allow a developer to realize systems that require evolution (Phylogenesis), development (Ontogenesis), and/or learning (Epigenesis). A dynamic routing algorithm has been added to a structure similar to that of common commercial FPGAs, in order to allow the creation of data paths between cells. As the creation of these paths is dynamic, it is possible to add new cells or to repair faulty ones at runtime.

Benchmarking therapeutic drug monitoring software: a review of available computer tools.

Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.