The British Journal of Clinical Pharmacology: The first 50 years.

The British Journal of Clinical Pharmacology celebrates its 50th anniversary of publication in 2023. Here four previous Editors-in-Chief and the current Editor reflect on the Journal's history and the changes that have occurred during that time.

[Clinical research: who owns the data?] / Klinisch geneesmiddelenonderzoek.

The data obtained in a clinical trial are facts and cannot formally be owned by anybody. However, knowledge that results from these facts can be proprietary and represents considerable commercial value. Clinical research with medicines or devices is often a collaboration between scientists and commercial companies, and this may generate a dilemma. The company may want to keep the knowledge hidden, for instance to obtain patents, whilst the scientist requires rapid publication. In the Netherlands this is resolved as much as possible by a contractual agreement. This codifies the formal position between a company and a medical center. However, the theory does not always agree with the practical situation in which many loopholes exist. These are discussed in the paper, based upon a hypothetical case.

Question-based development of high-risk medical devices: A proposal for a structured design and review process.

INTRODUCTION: The recent introduction of the European Medical Device Regulation poses stricter legislation for manufacturers developing medical devices in the EU. Many devices have been placed into a higher risk category, thus requiring more data before market approval, and a much larger focus has been placed on safety. For implantable and Class III devices, the highest risk class, clinical evidence is a necessity. However, the requirements of clinical study design and developmental outcomes are only described in general terms due to the diversity of devices. METHODS: A structured approach to determining the requirements for the clinical development of high-risk medical devices is introduced, utilizing the question-based development framework, which is already used for pharmaceutical drug development. An example of a novel implantable device for haemodialysis demonstrates how to set up a relevant target product profile defining the device requirements and criteria. The framework can be used in the medical device design phase to define specific questions to be answered during the ensuing clinical development, based upon five general questions, specified by the question-based framework. RESULTS: The result is a clear and evaluable overview of requirements and methodologies to verify and track these requirements in the clinical development phase. Development organizations will be guided to the optimal route, also to abandon projects destined for failure early on to minimize development risks. CONCLUSION: The framework could facilitate communication with funding agencies, regulators and clinicians, while highlighting remaining 'known unknowns' that require answering in the post-market phase after sufficient benefit is established relative to the risks.

Mitochondrial function, grip strength, and activity are related to recovery of mobility after a total knee arthroplasty.

Low muscle quality and a sedentary lifestyle are indicators for a slow recovery after a total knee arthroplasty (TKA). Mitochondrial function is an important part of muscle quality and a key driver of sarcopenia. However, it is not known whether it relates to recovery. In this pilot study, we monitored activity after TKA using a wrist mounted activity tracker and assessed the relation of mitochondrial function on the rate of recovery after TKA. Additionally, we compared the increase in activity as a way to measure recovery to traditional outcome measures. Patients were studied 2 weeks before TKA and up to 6 months after. Activity was monitored continuously. Baseline mitochondrial function (citrate synthase and complex [CP] 1-5 abundance of the electron transport chain) was determined on muscle tissue taken during TKA. Traditional outcome measures (Knee Injury and Osteoarthritis Outcome Score [KOOS], timed up-and-go [TUG] completion time, grip, and quadriceps strength) were performed 2 weeks before, 6 weeks after, and 6 months after TKA. Using a multivariate regression model with various clinical baseline parameters, the following were significantly related to recovery: CP5 abundance, grip strength, and activity (regression weights 0.13, 0.02, and 2.89, respectively). During recovery, activity correlated to the KOOS-activities of daily living (ADL) score (r = 0.55, p = 0.009) and TUG completion time (r = -0.61, p = 0.001). Mitochondrial function seems to be related to recovery, but so are activity and grip strength, all indicators of sarcopenia. Using activity trackers before and after TKA might give the surgeon valuable information on the expected recovery and the opportunity to intervene if recovery is low.

Objective Home-Monitoring of Physical Activity, Cardiovascular Parameters, and Sleep in Pediatric Obesity.

Introduction: Clinical research and treatment of childhood obesity is challenging, and objective biomarkers obtained in a home-setting are needed. The aim of this study was to determine the potential of novel digital endpoints gathered by a home-monitoring platform in pediatric obesity. Methods: In this prospective observational study, 28 children with obesity aged 6-16 years were included and monitored for 28 days. Patients wore a smartwatch, which measured physical activity (PA), heart rate (HR), and sleep. Furthermore, daily blood pressure (BP) measurements were performed. Data from 128 healthy children were utilized for comparison. Differences between patients and controls were assessed via linear mixed effect models. Results: Data from 28 patients (average age 11.6 years, 46% male, average body mass index 30.9) and 128 controls (average age 11.1 years, 46% male, average body mass index 18.0) were analyzed. Patients were recruited between November 2018 and February 2020. For patients, the median compliance for the measurements ranged from 55% to 100% and the highest median compliance was observed for the smartwatch-related measurements (81-100%). Patients had a lower daily PA level (4,597 steps vs. 6,081 steps, 95% confidence interval [CI] 862-2,108) and peak PA level (1,115 steps vs. 1,392 steps, 95% CI 136-417), a higher nighttime HR (81 bpm vs. 71 bpm, 95% CI 6.3-12.3) and daytime HR (98 bpm vs. 88 bpm, 95% CI 7.6-12.6), a higher systolic BP (115 mm Hg vs. 104 mm Hg, 95% CI 8.1-14.5) and diastolic BP (76 mm Hg vs. 65 mm Hg, 95% CI 8.7-12.7), and a shorter sleep duration (difference 0.5 h, 95% CI 0.2-0.7) compared to controls. Conclusion: Remote monitoring via wearables in pediatric obesity has the potential to objectively measure the disease burden in the home-setting. The novel endpoints demonstrate significant differences in PA level, HR, BP, and sleep duration between patients and controls. Future studies are needed to determine the capacity of the novel digital endpoints to detect effect of interventions.

Dose escalations in phase I studies: Feasibility of interpreting blinded pharmacodynamic data.

AIMS: During phase I study conduct, blinded data are reviewed to predict the safety of increasing the dose level. The aim of the present study was to describe the probability that effects are observed in blinded evaluations of data in a simulated phase I study design. METHODS: An application was created to simulate blinded pharmacological response curves over time for 6 common safety/efficacy measurements in phase I studies for 1 or 2 cohorts (6 active, 2 placebo per cohort). Effect sizes between 0 and 3 between-measurement standard deviations (SDs) were simulated. Each set of simulated graphs contained the individual response and mean ± SD over time. Reviewers (n = 34) reviewed a median of 100 simulated datasets and indicated whether an effect was present. RESULTS: Increasing effect sizes resulted in a higher chance of the effect being identified by the blinded reviewer. On average, 6% of effect sizes of 0.5 between-measurement SD were correctly identified, increasing to 72% in 3.0 between-measurement SD effect sizes. In contrast, on average 92-95% of simulations with no effect were correctly identified, with little effect of between-measurement variability in single cohort simulations. Adding a dataset of a second cohort at half the simulated dose did not appear to improve the interpretation. CONCLUSION: Our analysis showed that effect sizes <2× the between-measurement SD of the investigated outcome frequently go unnoticed by blinded reviewers, indicating that the weight given to these blinded analyses in current phase I practice is inappropriate and should be re-evaluated.

Cardiac age detected by machine learning applied to the surface ECG of healthy subjects: Creation of a benchmark.

OBJECTIVE: The aim of the present study was to develop a neural network to characterize the effect of aging on the ECG in healthy volunteers. Moreover, the impact of the various ECG features on aging was evaluated. METHODS & RESULTS: A total of 6228 healthy subjects without structural heart disease were included in this study. A neural network regression model was created to predict age of the subjects based on their ECG; 577 parameters derived from a 12­lead ECG of each subject were used to develop and validate the neural network; A tenfold cross-validation was performed, using 118 subjects for validation each fold. Using SHapley Additive exPlanations values the impact of the individual features on the prediction of age was determined. Of 6228 subjects tested, 1808 (29%) were females and mean age was 34 years, range 18-75 years. Physiologic age was estimated as a continuous variable with an average error of 6.9 ± 5.6 years (R2 = 0.72 ± 0.04). The correlation was slightly stronger for men (R2 = 0.74) than for women (R2 = 0.66). The most important features on the prediction of physiologic age were T wave morphology indices in leads V4 and V5, and P wave amplitude in leads AVR and II. CONCLUSION: The application of machine learning to the ECG using a neural network regression model, allows accurate estimation of physiologic cardiac age. This technique could be used to pick up subtle age-related cardiac changes, but also estimate the reversing of these age-associated effects by administered treatments.

Development and technical validation of a smartphone-based pediatric cough detection algorithm.

INTRODUCTION: Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children. METHODS: The training set was composed of 3228 pediatric cough sounds and 480,780 noncough sounds from various publicly available sources and continuous sound recordings of 7 patients admitted due to respiratory disease. A Gradient Boost Classifier was fitted on the training data, which was subsequently validated on recordings from 14 additional patients aged 0-14 admitted to the pediatric ward due to respiratory disease. The robustness of the algorithm was investigated by repeatedly classifying a recording with the smartphone-based algorithm during various conditions. RESULTS: The final algorithm obtained an accuracy of 99.7%, sensitivity of 47.6%, specificity of 99.96%, positive predictive value of 82.2% and negative predictive value 99.8% in the validation dataset. The correlation coefficient between manual- and automated cough counts in the validation dataset was 0.97 (p < .001). The intra- and interdevice reliability of the algorithm was adequate, and the algorithm performed best at an unobstructed distance of 0.5-1 m from the audio source. CONCLUSION: This novel smartphone-based pediatric cough detection application can be used for longitudinal follow-up in clinical care or as digital endpoint in clinical trials.

Population pharmacokinetics of clonazepam in saliva and plasma: Steps towards noninvasive pharmacokinetic studies in vulnerable populations.

AIM: Traditional studies focusing on the relationship between pharmacokinetics (PK) and pharmacodynamics necessitate blood draws, which are too invasive for children or other vulnerable populations. A potential solution is to use noninvasive sampling matrices, such as saliva. The aim of this study was to develop a population PK model describing the relationship between plasma and saliva clonazepam kinetics and assess whether the model can be used to determine trough plasma concentrations based on saliva samples. METHODS: Twenty healthy subjects, aged 18-30, were recruited and administered 0.5 or 1 mg of clonazepam solution. Paired plasma and saliva samples were obtained until 48 hours post-dose. A population pharmacokinetic model was developed describing the PK of clonazepam in plasma and the relationship between plasma and saliva concentrations. Bayesian maximum a posteriori optimization was applied to estimate the predictive accuracy of the model. RESULTS: A two-compartment distribution model best characterized clonazepam plasma kinetics with a mixture component on the absorption rate constants. Oral administration of the clonazepam solution caused contamination of the saliva compartment during the first 4 hours post-dose, after which the concentrations were driven by the plasma concentrations. Simulations demonstrated that the lower and upper limits of agreements between true and predicted plasma concentrations were -28% to 36% with one saliva sample. Increasing the number of saliva samples improved these limits to -18% to 17%. CONCLUSION: The developed model described the salivary and plasma kinetics of clonazepam, and could predict steady-state trough plasma concentrations based on saliva concentrations with acceptable accuracy.

Dealing with doping. A plea for better science, governance and education.

The creation of WADA contributed to harmonization of anti-doping and changed doping behavior and prevalence in the past 22 years. However, the system has developed important deficiencies and limitations that are causing harm to sports, athletes and society. These issues are related to the lack of evidence for most substances on the Prohibited List for performance or negative health effects, a lack of transparency and accountability of governance and decision-making by WADA and the extension of anti-doping policies outside the field of professional sports. This article tries to identify these deficiencies and limitations and presents a plea for more science, better governance and more education. This should lead to a discussion for reform among stakeholders, which should cover support of a new Prohibited List by actual research and evidence and introduce better governance with accountable control bodies and regulation. Finally, comprehensive education for all stakeholders will be the basis of all future positive improvements.

Clinical validation of digital biomarkers for paediatric patients with asthma and cystic fibrosis: potential for clinical trials and clinical care.

BACKGROUND: Digital biomarkers are a promising novel method to capture clinical data in a home setting. However, clinical validation prior to implementation is of vital importance. The aim of this study was to clinically validate physical activity, heart rate, sleep and forced expiratory volume in 1 s (FEV1) as digital biomarkers measured by a smartwatch and portable spirometer in children with asthma and cystic fibrosis (CF). METHODS: This was a prospective cohort study including 60 children with asthma and 30 children with CF (aged 6-16 years). Participants wore a smartwatch, performed daily spirometry at home and completed a daily symptom questionnaire for 28 days. Physical activity, heart rate, sleep and FEV1 were considered candidate digital end-points. Data from 128 healthy children were used for comparison. Reported outcomes were compliance, difference between patients and controls, correlation with disease activity, and potential to detect clinical events. Analysis was performed with linear mixed effects models. RESULTS: Median compliance was 88%. On average, patients exhibited lower physical activity and FEV1 compared with healthy children, whereas the heart rate of children with asthma was higher compared with healthy children. Days with a higher symptom score were associated with lower physical activity for children with uncontrolled asthma and CF. Furthermore, FEV1 was lower and (nocturnal) heart rate was higher for both patient groups on days with more symptoms. Candidate biomarkers appeared able to describe a pulmonary exacerbation. CONCLUSIONS: Portable spirometer- and smartwatch-derived digital biomarkers show promise as candidate end-points for use in clinical trials or clinical care in paediatric lung disease.

Accelerating vaccine trial conduct in a pandemic with a hot spot-based inclusion strategy using trial and epidemic simulation.

Clinical development of vaccines in a pandemic situation should be rigorous but expedited to tackle the pandemic threat as fast as possible. We explored the effects of a novel vaccine trial strategy that actively identifies and enrolls subjects in local areas with high infection rates. In addition, we assessed the practical requirements needed for such a strategy. Clinical trial simulations were used to assess the effects of utilizing these so-called "hot spot strategy" compared to a traditional vaccine field trial. We used preset parameters of a pandemic outbreak and incorporated realistic aspects of conducting a trial in a pandemic setting. Our simulations demonstrated that incorporating a hot spot strategy shortened the duration of the vaccine trial considerably, even if only one hot spot was identified during the clinical trial. The active hot spot strategy described in this paper has clear advantages compared to a "wait-and-see" approach that is used in traditional vaccine efficacy trials. Completion of a clinical trial can be expedited by adapting to resurgences and outbreaks that will occur in a population during a pandemic. However, this approach requires a speed of response that is unusual for a traditional phase III clinical trial. Therefore, several recommendations are made to help accomplish rapid clinical trial setup in areas identified as local outbreaks. The described model and hot spot vaccination strategy can be adjusted to disease-specific transmission characteristics and could therefore be applied to any future pandemic threat.

Theoretical Performance of Nonlinear Mixed-Effect Models Incorporating Saliva as an Alternative Sampling Matrix for Therapeutic Drug Monitoring in Pediatrics: A Simulation Study.

BACKGROUND: Historically, pharmacokinetic (PK) studies and therapeutic drug monitoring (TDM) have relied on plasma as a sampling matrix. Noninvasive sampling matrices, such as saliva, can reduce the burden on pediatric patients. The variable plasma-saliva relationship can be quantified using population PK models (nonlinear mixed-effect models). However, criteria regarding acceptable levels of variability in such models remain unclear. In this simulation study, the authors aimed to propose a saliva TDM evaluation framework and evaluate model requirements in the context of TDM, with gentamicin and lamotrigine as model compounds. METHODS: Two population pharmacokinetic models for gentamicin in neonates and lamotrigine in pediatrics were extended with a saliva compartment including a delay constant (kSALIVA), a saliva:plasma ratio, and between-subject variability (BSV) on both parameters. Subjects were simulated using a realistic covariate distribution. Bayesian maximum a posteriori TDM was applied to assess the performance of an increasing number of TDM saliva samples and varying levels of BSV and residual variability. Saliva TDM performance was compared with plasma TDM performance. The framework was applied to a known voriconazole saliva model as a case study. RESULTS: TDM performed using saliva resulted in higher target attainment than no TDM, and a residual proportional error <25% on saliva observations led to saliva TDM performance comparable with plasma TDM. BSV on kSALIVA did not affect performance, whereas increasing BSV on saliva:plasma ratios by >25% for gentamicin and >50% for lamotrigine reduced performance. The simulated target attainment for voriconazole saliva TDM was >90%. CONCLUSIONS: Saliva as an alternative matrix for noninvasive TDM is possible using nonlinear mixed-effect models combined with Bayesian optimization. This article provides a workflow to explore TDM performance for compounds measured in saliva and can be used for evaluation during model building.

Postdischarge Recovery after Acute Pediatric Lung Disease Can Be Quantified with Digital Biomarkers.

BACKGROUND: Pediatric patients admitted for acute lung disease are treated and monitored in the hospital, after which full recovery is achieved at home. Many studies report in-hospital recovery, but little is known regarding the time to full recovery after hospital discharge. Technological innovations have led to increased interest in home-monitoring and digital biomarkers. The aim of this study was to describe at-home recovery of 3 common pediatric respiratory diseases using a questionnaire and wearable device. METHODS: In this study, patients admitted due to pneumonia (n = 30), preschool wheezing (n = 30), and asthma exacerbation (AE; n = 11) were included. Patients were monitored with a smartwatch and a questionnaire during admission, with a 14-day recovery period and a 10-day "healthy" period. Median compliance was calculated, and a mixed-effects model was fitted for physical activity and heart rate (HR) to describe the recovery period, and the physical activity recovery trajectory was correlated to respiratory symptom scores. RESULTS: Median compliance was 47% (interquartile range [IQR] 33-81%) during the entire study period, 68% (IQR 54-91%) during the recovery period, and 28% (IQR 0-74%) during the healthy period. Patients with pneumonia reached normal physical activity 12 days postdischarge, while subjects with wheezing and AE reached this level after 5 and 6 days, respectively. Estimated mean physical activity was closely correlated with the estimated mean symptom score. HR measured by the smartwatch showed a similar recovery trajectory for subjects with wheezing and asthma, but not for subjects with pneumonia. CONCLUSIONS: The digital biomarkers, physical activity, and HR obtained via smartwatch show promise for quantifying postdischarge recovery in a noninvasive manner, which can be useful in pediatric clinical trials and clinical care.

Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm.

Introduction: The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying. Methods: For the development of the algorithm a training dataset containing 897 5-s clips of crying infants and 1,263 clips of non-crying infants and common domestic sounds was assembled from various online sources. OpenSMILE software was used to extract 1,591 audio features per audio clip. A random forest classifying algorithm was fitted to identify crying from non-crying in each audio clip. For the validation of the algorithm, an independent dataset consisting of real-life recordings of 15 infants was used. A 29-min audio clip was analyzed repeatedly and under differing circumstances to determine the intra- and inter- device repeatability and robustness of the algorithm. Results: The algorithm obtained an accuracy of 94% in the training dataset and 99% in the validation dataset. The sensitivity in the validation dataset was 83%, with a specificity of 99% and a positive- and negative predictive value of 75 and 100%, respectively. Reliability of the algorithm appeared to be robust within- and across devices, and the performance was robust to distance from the sound source and barriers between the sound source and the microphone. Conclusion: The algorithm was accurate in detecting cry duration and was robust to various changes in ambient settings.