On optimal designs for clinical trials: An updated review

Optimization of clinical trial designs can help investigators achieve higher qualityresults for the given resource constraints. The present paper gives an overviewof optimal designs for various important problems that arise in different stages ofclinical drug development, including phase I dose–toxicity studies; phase I/II studiesthat consider early efficacy and toxicity outcomes simultaneously; phase IIdose–response studies driven by multiple comparisons (MCP), modeling techniques(Mod), or their combination (MCP–Mod); phase III randomized controlled multiarmmulti-objective clinical trials to test difference among several treatment groups;and population pharmacokinetics–pharmacodynamics experiments. We find thatmodern literature is very rich with optimal design methodologies that can be utilizedby clinical researchers to improve efficiency of drug development

Study of the Influence of a Combination of Pharmacogenetic Variables on Tacrolimus Exposure: A Population Pharmacokinetic Approach

[eng] The calcineurin inhibitor Tacrolimus (Tac) is used to prevent acute rejection after renal transplant. Unfortunately, the clinical use of Tac is complicated by its considerable toxicity, narrow therapeutic window, and high interindividual pharmacokinetic variability. Therapeutic drug monitoring is commonly applied to individualize Tac therapy in renal transplant recipients using trough concentrations. When concentrations are out of the target range, the physicians roughly estimate what should be the appropriate change of dose. Despite trough concentrations are the most used exposure parameters, the Area Under the Curve (AUC) correlates better with the clinical outcomes. In the clinical setting, an AUC tiered-dosing is not feasible, thus an alternate approach is that based on limited-sampling strategy by means of Bayesian prediction. In this sense, the use of a population pharmacokinetic (PPK) model can assist for the first dose calculation at the start of treatment but also for dose adaptation based on predefined target by means of Maximum A Posteriori Bayesian forecasting technique, supporting therapeutic drug monitoring. Recent discovery of new polymorphism has led to further investigations on that file aiming to reduce the unexplained interindividual variability in Tacrolimus exposure. The main objective of the present work was to design a population-based Bayesian prediction tool for initial dose calculation and dose adaptation during the post-transplant period through: 1. Characterizing the Tacrolimus population PK using an intensive sampling and confirming the best limiting sampling strategy to be applied during dose adaptation. 2. To deeply Investigate in tacrolimus pharmacogenetic predictors of interindividual variability 3. Implementing new genetic information as well as other clinical factors to generate a refined population pharmacokinetic model reducing unexplained variability. A Tacrolimus population PK model was designed to characterize accurately the population absorption phase as well as quantify the inter and intra-individual variability. The first population PK model led to obtain an optimal sampling strategy using only trough concentrations for dose tailoring through Bayesian prediction. The CYP3A4*22 and CYP3A5*3 alleles are all independently associated with Tac exposure during the first year after transplantation. Proofs that a combined CYP3A4 and 5 genotype cluster is of relevant importance when deciding on the initial Tac dose. Poor metabolizers patients related to the cluster of CYP3A4*1/*22 and CYP3A5*3/*3, had lower dose requirements to achieve the target concentrations. Extensive metabolizers patients related to the cluster of CYP3A4*1/*1 and CYP3A5*1/*3, had higher dose requirements to achieve the target concentrations. A new refined PPK model was then developed using the combination of the cluster of CYP3A5*3 and CYP3A4*22 polymorphisms, age and hematocrit to describe Tacrolimus pharmacokinetics. The CYP3A extensive metabolizers patients may require about 2-fold higher doses compared to poor metabolizers. Moreover, intermediate metabolizers may require about 1.5-fold higher doses compared to poor metabolizers.[cat] El Tacrolimus (Tac), un inhibidor de la calcineurina, s’utilitza per prevenir el rebuig agut en transplantament renal. La utilització en clínica del Tac resulta complicada degut a la seva considerable toxicitat, marge terapèutic estret i elevada variabilitat interindividual en la seva farmacocinètica. La monitorització de les concentracions mínimes del Tac s’utilitza habitualment en la individualització de dosi. No obstant, quan els valors de concentracions estan fora del marge terapèutic, les correccions de dosi es realitzen de manera empírica. La utilització de un model farmacocinètic (PK) poblacional del Tac pot ajudar al càlcul per la dosi inicial així com en l’ajust de dosi posterior a través d’eines de predicció Bayesiana que donin suport a la monitorització terapèutica. Recents descobriments en nous polimorfisme que afecten el metabolisme del Tac han portat a noves investigacions per tal de reduir la variabilitat inexplicada en l’exposició del Tac. L’objectiu principal d’aquesta tesi doctoral ha sigut dissenyar un eina poblacional de predicció Bayesiana pel càlcul de la dosi inicial de Tac i futures canvis de dosi durant el període post-trasplant: 1. Caracteritzant la farmacocinètica poblacional del Tacrolimus utilitzant un mostreig intensiu i confirmant la millor estratègia de mostreig durant la fase d’adaptació de la dosi 2. Investigant els predictors farmacogenòmics de la variabilitat interindividual del Tac 3. Implementant les noves informacions genètiques així com altres factors clínics per tal de refinar un nou model de farmacocinètica poblacional reduint la variabilitat inexplicada. Un model de PK poblacional del Tac va ser dissenyat per tal de definir acuradament la fase d’absorció i quantificar les variabilitats intra- e inter-individuals. La utilització de mostrejar concentracions pre-dosi va demostrar-se com una òptima estratègia de mostreig. Els polimorfismes CYP3A4*22 i CYP3A5*3 van demostrar-se independentment associats a l’exposició de Tac durant el primer any post-trasplant. La combinació dels dos polimorfismes es rellevant en l’elecció de la dosi inicial de Tac. S’ha dissenyat un nou model PK poblacional combinant un nou clúster dels polimorfismes CYP3A4*22 I CYP3A5*3, edat I hematòcrit per descriure la PK del Tac. Els metabolitzadors extensius pel CYP3A poden requerir aproximadament el doble de dosi respecte els metabolizadors lents

Pharmacists as the delivery channel for adherence support in asthma

Non-adherence to inhaled corticosteroids remains a key challenge in asthma care in the United Kingdom (UK) – it increases healthcare costs, morbidity, and mortality. The growing pressure on UK primary care increased interest in pharmacists as a potential delivery channel for adherence support. However, research on UK pharmacist-led adherence support for asthma is limited. // This thesis addresses the gap in the literature by examining the effectiveness of previous pharmacist-led interventions in improving adherence in adults with asthma (systematic review/meta-analysis, 11 studies), exploring the perspectives of UK pharmacists (online questionnaire, n = 127) and adults with asthma (qualitative study, n = 17) on pharmacist-led adherence support for asthma, and assessing the feasibility and acceptability of a new pharmacist-led adherence support intervention delivered to adults with asthma in general practice (before-and-after study, n = 31). // Previous pharmacist-led interventions significantly improved adherence in adults with asthma (d = 0.49, 95% CI 0.35 – 0.64, p < 0.0001), with effective interventions addressing the ability and motivation to adhere to medication. UK pharmacists reported feeling most confident in and focusing mostly on patient education as adherence support (i.e. ability-related processes). Adults with asthma used their trust in other healthcare professionals (e.g. general practitioners) to gauge their trust in pharmacists. While they were open to pharmacist-led support due to gaps in existing asthma care, they were also concerned about pharmacist competency and role overlap with other healthcare professionals. The new pharmacist-led adherence intervention delivered in general practice demonstrated high acceptability among pharmacists and adults with asthma, but further work is needed to improve the feasibility of the study design. // This research suggests that pharmacist-led adherence support is worth exploring further. With additional adherence-focused support/training for pharmacists and public awareness of pharmacist-led care, UK pharmacists may be able to make a valuable contribution to asthma care

Modelling solid/fluid interactions in hydrodynamic flows: a hybrid multiscale approach

With the advent of high performance computing (HPC), we can simulate nature at time and length scales that we could only dream of a few decades ago. Through the development of theory and numerical methods in the last fifty years, we have at our disposal a plethora of mathematical and computational tools to make powerful predictions about the world which surrounds us. From quantum methods like Density Functional Theory (DFT); going through atomistic methods such as Molecular Dynamics (MD) and Monte Carlo (MC), right up to more traditional macroscopic techniques based on Partial Differential Equations (PDEs) discretization like the Finite Element Method (FEM) or Finite Volume Method (FVM), which are respectively, the foundation of computational Structural Analysis and Computational Fluid Dynamics (CFD). Many modern scientific computing challenges in physics stem from combining appropriately two or more of these methods, in order to tackle problems that could not be solved otherwise using just one of them alone. This is known as multi-scale modeling, which aims to achieve a trade-off between computational cost and accuracy by combining two or more physical models at different scales. In this work, a multi-scale domain decomposition technique based on coupling MD and CFD methods, has been developed to make affordable the study of slip and friction, with atomistic detail, at length scales otherwise impossible by fully atomistic methods alone. A software framework has been developed to facilitate the execution of this particular kind of simulations on HPC clusters. This have been possible by employing the in-house developed CPL_LIBRARY software library, which provides key functionality to implement coupling through domain decomposition.Open Acces

EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier handling is vital for predictive maintenance as the variables distribution initially showed a right-skewed pattern but after rectifying, it became more centralized, with correlations between specific sensors showing potential for further exploration. Q2) Data balancing through SMOTE and feature engineering is essential due to the rarity of actual failure instances. Substantial challenges are observed when predicting \u27Failure\u27 instances, with a lower true positive rate (73%), resulting in low precision (0.02) and recall (0.73) for \u27Failure\u27 predictions. This is further reflected in the low F1-Score (0.03) for \u27Failure,\u27 indicating a trade-off between precision and recall. Despite a commendable overall accuracy of 94%, the class imbalance within the dataset (92,200 \u27Running\u27 instances vs. 126 \u27Failure\u27 instances) remains a contributing factor to the model\u27s limitations. Q3) Machine learning algorithm performance varies, with Catboost excelling in accuracy and failure detection. The choice of algorithm and continuous model refinement are critical for enhanced predictive accuracy in industrial contexts. The main conclusions are: Q1) Addressing outliers in data preprocessing significantly enhances the accuracy of machine failure prediction models. Q2) focuses on addressing the issue of equipment failure parameter imbalance. It was found in the research findings that there was a significant imbalance in the failure data, with only 0.14% of the dataset representing actual failures and 99.86% of the dataset pertaining to non-failure data. This extreme class disparity can result in biased models that underperform on underrepresented classes, which is a common problem in machine learning. Q3) Catboost outperforms other algorithms in predicting machine failures with remarkable accuracy and failure detection rates of 92% accuracy and 99% times it is correct, and further exploration of diverse data and algorithms is needed for tailored industrial applications. Future research areas include advanced outlier handling, sensor relationships, and data balancing for improved model accuracy. Addressing rare failures, enhancing model performance, and exploring diverse machine learning algorithms are critical for advancing predictive maintenance

Applied Harmonic Analysis and Data Processing

Massive data sets have their own architecture. Each data source has an inherent structure, which we should attempt to detect in order to utilize it for applications, such as denoising, clustering, anomaly detection, knowledge extraction, or classification. Harmonic analysis revolves around creating new structures for decomposition, rearrangement and reconstruction of operators and functions—in other words inventing and exploring new architectures for information and inference. Two previous very successful workshops on applied harmonic analysis and sparse approximation have taken place in 2012 and in 2015. This workshop was the an evolution and continuation of these workshops and intended to bring together world leading experts in applied harmonic analysis, data analysis, optimization, statistics, and machine learning to report on recent developments, and to foster new developments and collaborations

Machine learning and computational methods to identify molecular and clinical markers for complex diseases – case studies in cancer and obesity

In biomedical research, applied machine learning and bioinformatics are the essential disciplines heavily involved in translating data-driven findings into medical practice. This task is especially accomplished by developing computational tools and algorithms assisting in detection and clarification of underlying causes of the diseases. The continuous advancements in high-throughput technologies coupled with the recently promoted data sharing policies have contributed to presence of a massive wealth of data with remarkable potential to improve human health care. In concordance with this massive boost in data production, innovative data analysis tools and methods are required to meet the growing demand. The data analyzed by bioinformaticians and computational biology experts can be broadly divided into molecular and conventional clinical data categories. The aim of this thesis was to develop novel statistical and machine learning tools and to incorporate the existing state-of-the-art methods to analyze bio-clinical data with medical applications. The findings of the studies demonstrate the impact of computational approaches in clinical decision making by improving patients risk stratification and prediction of disease outcomes. This thesis is comprised of five studies explaining method development for 1) genomic data, 2) conventional clinical data and 3) integration of genomic and clinical data. With genomic data, the main focus is detection of differentially expressed genes as the most common task in transcriptome profiling projects. In addition to reviewing available differential expression tools, a data-adaptive statistical method called Reproducibility Optimized Test Statistic (ROTS) is proposed for detecting differential expression in RNA-sequencing studies. In order to prove the efficacy of ROTS in real biomedical applications, the method is used to identify prognostic markers in clear cell renal cell carcinoma (ccRCC). In addition to previously known markers, novel genes with potential prognostic and therapeutic role in ccRCC are detected. For conventional clinical data, ensemble based predictive models are developed to provide clinical decision support in treatment of patients with metastatic castration resistant prostate cancer (mCRPC). The proposed predictive models cover treatment and survival stratification tasks for both trial-based and realworld patient cohorts. Finally, genomic and conventional clinical data are integrated to demonstrate the importance of inclusion of genomic data in predictive ability of clinical models. Again, utilizing ensemble-based learners, a novel model is proposed to predict adulthood obesity using both genetic and social-environmental factors. Overall, the ultimate objective of this work is to demonstrate the importance of clinical bioinformatics and machine learning for bio-clinical marker discovery in complex disease with high heterogeneity. In case of cancer, the interpretability of clinical models strongly depends on predictive markers with high reproducibility supported by validation data. The discovery of these markers would increase chance of early detection and improve prognosis assessment and treatment choice