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.

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.

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.

Abstract

This paper introduces the ubichip, a custom reconfigurable electronic device capable of implementing bioinspired circuits featuring growth, learning, and evolution. The ubichip is developed in the framework of Perplexus, a European project that aims to develop a scalable hardware platform made of bio-inspired custom reconfigurable devices for simulating large-scale complex systems. In this paper, we describe the configurability and architectural mechanisms that will allow the implementation of evolvable and developmental cellular and neural systems in an efficient way. These mechanisms are dynamic routing, selfreconfiguration, and a neural-friendly logic cell's architecture.

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Abstract. In this paper we present a new dynamic routing algorithm specially implemented for 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. Specifically designed for implementing cellular applications, such as neural networks, this circuit is composed of two main parts: a two-dimensional array of basic elements similar to those found in common commercial FPGAs, and a two-dimensional array of routing units that implement a dynamic routing algorithm which allows the creation of data paths between cells at runtime.

Fault tolerance is a crucial operational aspect of biological systems and the self-repair capabilities of complex organisms far exceeds that of even the most advanced electronic devices. While many of the processes used by nature to achieve fault tolerance cannot easily be applied to silicon-based systems, in this paper we show that mechanisms loosely inspired by the operation of multicellular organisms can be transported to electronic systems to provide self-repair capabilities. Features such as dynamic routing, reconfiguration, and on-chip reprogramming can be invaluable for the realization of adaptive hardware systems and for the design of highly complex systems based on the kind of unreliable components that are likely to be introduced in the not-too-distant future. In this paper, we describe the implementation of fault tolerant features that address error detection and recovery through dynamic routing, reconfiguration, and on-chip reprogramming in a novel application specific integrated circuit. We take inspiration from three biological models: phylogenesis, ontogenesis, and epigenesis (hence the POE in POEtic). As in nature, our approach is based on a set of separate and complementary techniques that exploit the novel mechanisms provided by our device in the particular context of fault tolerance.