Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

Spontaneous recovery from neuromuscular block after a single dose of a muscle relaxant in pediatric patients: A systematic review using a network meta-analytic and meta-regression approach

BACKGROUND

Age-related differences in the pharmacokinetics and pharmacodynamics of neuromuscular blocking agents (NMBAs) and the short duration of many surgical procedures put pediatric patients at risk of postoperative residual curarization (PORC). To date, the duration of neuromuscular blocking agent effect in children has not been analyzed in a quantitative review. The current meta-analysis aimed to compare spontaneous recovery following administration of various types and doses of neuromuscular blocking agents and to quantify the effect of prognostic variables associated with the recovery time in pediatric patients.

METHOD

We searched for randomized controlled trials (RCTs) and controlled clinical trials (CCTs) that compared the time to 25% T1 (t25), from 25% to 75% T1 (RI25-75), and to ≥90% train-of-four (tTOF90) neuromuscular recovery between common neuromuscular blocking agent treatments administered as a single bolus to healthy pediatric participants. We compared spontaneous t25, RI25-75, and tTOF90 between (1) neuromuscular blocking agent treatments and (2) age groups receiving a given neuromuscular blocking agent intervention and anesthesia technique. Bayesian random-effects network and pairwise meta-analyses along with meta-regression were used to evaluate the results.

RESULTS

We used data from 71 randomized controlled trials/controlled clinical trials including 4319 participants. Network meta-analysis allowed for the juxtaposition and ranking of spontaneous t25, RI25-75, and tTOF90 following common neuromuscular blocking agent interventions. For all neuromuscular blocking agents a log-linear relationship between dose and duration of action was found. With the neuromuscular blocking agent treatments studied, the average tTOF90 (mean[CrI95]) in children (>2-11 y) was 41.96 [14.35, 69.50] and 17.06 [5.99, 28.30] min shorter than in neonates (<28 d) and infants (28 d-12 M), respectively. We found a negative log-linear correlation between age and duration of neuromuscular blocking agent effect. The difference in the tTOF90 (mean[CrI95]) between children and other age groups increased by 21.66 [8.82, 34.53] min with the use of aminosteroid neuromuscular blocking agents and by 24.73 [7.92, 41.43] min with the addition of sevoflurane/isoflurane for anesthesia maintenance.

CONCLUSIONS

The times to neuromuscular recovery are highly variable. These can decrease significantly with age and are prolonged when volatile anesthetics are administered. This variability, combined with the short duration of many pediatric surgical procedures, makes quantitative neuromuscular monitoring mandatory even after a single dose of neuromuscular blocking agent.

Research Gaps in Pediatric Heart Failure: Defining the Gaps and Then Closing Them Over the Next Decade

Given the numerous opportunities and the wide knowledge gaps in pediatric heart failure, an international group of pediatric heart failure experts with diverse backgrounds were invited and tasked with identifying research gaps in each pediatric heart failure domain that scientists and funding agencies need to focus on over the next decade.

Advances in pharmacokinetic-pharmacodynamic modelling for pediatric drug development: extrapolations and exposure-response analyses

INTRODUCTION

Pharmacokinetic (PK)-Pharmacodynamic (PD) and exposure-response (E-R) modeling are critical parts of pediatric drug development. By integrating available knowledge and supportive data to support the design of future studies and pediatric dose selection, these techniques increase the efficiency of pediatric drug development and lowers the risk of exposing pediatric study participants to suboptimal or unsafe dose regimens.

AREAS COVERED

The role of PK, PK-PD and E-R modeling within pediatric drug development and pediatric dose selection is discussed. These models allow investigation of the impact of age and bodyweight on PK and PD in children, despite the often sparse data on the pediatric population. Also discussed is how E-R analyses strengthen the evidence basis to support (full or partial) extrapolation of drug efficacy from adults to children, and between different pediatric age groups.

EXPERT OPINION

Accelerated pediatric drug development and optimized pediatric dosing guidelines are expected from three future developments: (1) Increased focus on E-R modeling of currently approved drugs in children resulting in (novel) E-R modeling techniques and best practices, (2) increased use of real-world data for E-R (3) increased implementation of available population PK and E-R information in pediatric drug dosing guidelines.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence

BACKGROUND

With the development of Artificial Intelligence (AI) techniques, smart health monitoring, particularly neonatal cardiorespiratory monitoring with wearable devices, is becoming more popular. To this end, it is crucial to investigate the trend of AI and wearable sensors being developed in this domain.

METHODS

We performed a review of papers published in IEEE Xplore, Scopus, and PubMed from the year 2000 onwards, to understand the use of AI for neonatal cardiorespiratory monitoring with wearable technologies. We reviewed the advances in AI development for this application and potential future directions. For this review, we assimilated machine learning (ML) algorithms developed for neonatal cardiorespiratory monitoring, designed a taxonomy, and categorised the methods based on their learning capabilities and performance.

RESULTS

For AI related to wearable technologies for neonatal cardio-respiratory monitoring, 63% of studies utilised traditional ML techniques and 35% utilised deep learning techniques, including 6% that applied transfer learning on pre-trained models.

CONCLUSIONS

A detailed review of AI methods for neonatal cardiorespiratory wearable sensors is presented along with their advantages and disadvantages. Hierarchical models and suggestions for future developments are highlighted to translate these AI technologies into patient benefit.

IMPACT

State-of-the-art review in artificial intelligence used for wearable neonatal cardiorespiratory monitoring. Taxonomy design for artificial intelligence methods. Comparative study of AI methods based on their advantages and disadvantages.

A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Locating Youth Exposed to Parental Justice Involvement in the Electronic Health Record: Development of a Natural Language Processing Model

BACKGROUND

Parental justice involvement (eg, prison, jail, parole, or probation) is an unfortunately common and disruptive household adversity for many US youths, disproportionately affecting families of color and rural families. Data on this adversity has not been captured routinely in pediatric health care settings, and if it is, it is not discrete nor able to be readily analyzed for purposes of research.

OBJECTIVE

In this study, we outline our process training a state-of-the-art natural language processing model using unstructured clinician notes of one large pediatric health system to identify patients who have experienced a justice-involved parent.

METHODS

Using the electronic health record database of a large Midwestern pediatric hospital-based institution from 2011-2019, we located clinician notes (of any type and written by any type of provider) that were likely to contain such evidence of family justice involvement via a justice-keyword search (eg, prison and jail). To train and validate the model, we used a labeled data set of 7500 clinician notes identifying whether the patient was ever exposed to parental justice involvement. We calculated the precision and recall of the model and compared those rates to the keyword search.

RESULTS

The development of the machine learning model increased the precision (positive predictive value) of locating children affected by parental justice involvement in the electronic health record from 61% (a simple keyword search) to 92%.

CONCLUSIONS

The use of machine learning may be a feasible approach to addressing the gaps in our understanding of the health and health services of underrepresented youth who encounter childhood adversities not routinely captured-particularly for children of justice-involved parents.

The Blind Spot of Pharmacology: A Scoping Review of Drug Metabolism in Prematurely Born Children

The limit for possible survival after extremely preterm birth has steadily improved and consequently, more premature neonates with increasingly lower gestational age at birth now require care. This specialized care often include intensive pharmacological treatment, yet there is currently insufficient knowledge of gestational age dependent differences in drug metabolism. This potentially puts the preterm neonates at risk of receiving sub-optimal drug doses with a subsequent increased risk of adverse or insufficient drug effects, and often pediatricians are forced to prescribe medication as off-label or even off-science. In this review, we present some of the particularities of drug disposition and metabolism in preterm neonates. We highlight the challenges in pharmacometrics studies on hepatic drug metabolism in preterm and particularly extremely (less than 28 weeks of gestation) preterm neonates by conducting a scoping review of published literature. We find that >40% of included studies failed to report a clear distinction between term and preterm children in the presentation of results making direct interpretation for preterm neonates difficult. We present summarized findings of pharmacokinetic studies done on the major CYP sub-systems, but formal meta analyses were not possible due the overall heterogeneous approaches to measuring the phase I and II pathways metabolism in preterm neonates, often with use of opportunistic sampling. We find this to be a testament to the practical and ethical challenges in measuring pharmacokinetic activity in preterm neonates. The future calls for optimized designs in pharmacometrics studies, including PK/PD modeling-methods and other sample reducing techniques. Future studies should also preferably be a collaboration between neonatologists and clinical pharmacologists.

Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design

Interest in real-world data (RWD) and real-world evidence (RWE) to expedite and enrich the development of new biopharmaceutical products has proliferated in recent years, spurred by the 21st Century Cures Act in the United States and similar policy efforts in other countries, willingness by regulators to consider RWE in their decisions, demands from third-party payers, and growing concerns about the limitations of traditional clinical trials. Although much of the recent literature on RWE has focused on potential regulatory uses (e.g., product approvals in oncology or rare diseases based on single-arm trials with external control arms), this article reviews how biopharmaceutical companies can leverage RWE to inform internal decisions made throughout the product development process. Specifically, this article will review use of RWD to guide pipeline and portfolio strategy; use of novel sources of RWD to inform product development, use of RWD to inform clinical development, use of advanced analytics to harness "big" RWD, and considerations when using RWD to inform internal decisions. Topics discussed will include the use of molecular, clinicogenomic, medical imaging, radiomic, and patient-derived xenograft data to augment traditional sources of RWE, the use of RWD to inform clinical trial eligibility criteria, enrich trial population based on predicted response, select endpoints, estimate sample size, understand disease progression, and enhance diversity of participants, the growing use of data tokenization and advanced analytical techniques based on artificial intelligence in RWE, as well as the importance of data quality and methodological transparency in RWE.

Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

PURPOSE

The advent of clinically adapted machine learning algorithms can solve numerous problems ranging from disease diagnosis and prognosis to therapy recommendations. This systematic review examines the performance of machine learning (ML) algorithms and evaluates the progress made to date towards their implementation in clinical practice.

METHODS

Systematic searching of databases (PubMed, MEDLINE, Scopus, Google Scholar, Cochrane Library and WHO Covid-19 database) to identify original articles published between January 2011 and October 2021. Studies reporting ML techniques in clinical practice involving humans and ML algorithms with a performance metric were considered.

RESULTS

Of 873 unique articles identified, 36 studies were eligible for inclusion. The XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications (n = 7 studies); this was followed jointly by random forest algorithm, logistic regression, and the support vector machine, respectively (n = 5 studies). Prediction of outcomes (n = 33), in particular Inflammatory diseases (n = 7) received the most attention followed by cancer and neuropsychiatric disorders (n = 5 for each) and Covid-19 (n = 4). Thirty-three out of the thirty-six included studies passed more than 50% of the selected quality assessment criteria in the TRIPOD checklist. In contrast, none of the studies could achieve an ideal overall bias rating of 'low' based on the PROBAST checklist. In contrast, only three studies showed evidence of the deployment of ML algorithm(s) in clinical practice.

CONCLUSIONS

ML is potentially a reliable tool for clinical decision support. Although advocated widely in clinical practice, work is still in progress to validate clinically adapted ML algorithms. Improving quality standards, transparency, and interpretability of ML models will further lower the barriers to acceptability.

Integration of advanced methods and models to study drug absorption and related processes: An UNGAP perspective

This collection of contributions from the European Network on Understanding Gastrointestinal Absorption-related Processes (UNGAP) community assembly aims to provide information on some of the current and newer methods employed to study the behaviour of medicines. It is the product of interactions in the immediate pre-Covid period when UNGAP members were able to meet and set up workshops and to discuss progress across the disciplines. UNGAP activities are divided into work packages that cover special treatment populations, absorption processes in different regions of the gut, the development of advanced formulations and the integration of food and pharmaceutical scientists in the food-drug interface. This involves both new and established technical approaches in which we have attempted to define best practice and highlight areas where further research is needed. Over the last months we have been able to reflect on some of the key innovative approaches which we were tasked with mapping, including theoretical, in silico, in vitro, in vivo and ex vivo, preclinical and clinical approaches. This is the product of some of us in a snapshot of where UNGAP has travelled and what aspects of innovative technologies are important. It is not a comprehensive review of all methods used in research to study drug dissolution and absorption, but provides an ample panorama of current and advanced methods generally and potentially useful in this area. This collection starts from a consideration of advances in a priori approaches: an understanding of the molecular properties of the compound to predict biological characteristics relevant to absorption. The next four sections discuss a major activity in the UNGAP initiative, the pursuit of more representative conditions to study lumenal dissolution of drug formulations developed independently by academic teams. They are important because they illustrate examples of in vitro simulation systems that have begun to provide a useful understanding of formulation behaviour in the upper GI tract for industry. The Leuven team highlights the importance of the physiology of the digestive tract, as they describe the relevance of gastric and intestinal fluids on the behaviour of drugs along the tract. This provides the introduction to microdosing as an early tool to study drug disposition. Microdosing in oncology is starting to use gamma-emitting tracers, which provides a link through SPECT to the next section on nuclear medicine. The last two papers link the modelling approaches used by the pharmaceutical industry, in silico to Pop-PK linking to Darwich and Aarons, who provide discussion on pharmacometric modelling, completing the loop of molecule to man.

Pediatric data from the All of Us research program: demonstration of pediatric obesity over time

OBJECTIVE

To describe and demonstrate use of pediatric data collected by the All of Us Research Program.

MATERIALS AND METHODS

All of Us participant physical measurements and electronic health record (EHR) data were analyzed including investigation of trends in childhood obesity and correlation with adult body mass index (BMI).

RESULTS

We identified 19 729 participants with legacy pediatric EHR data including diagnoses, prescriptions, visits, procedures, and measurements gathered since 1980. We found an increase in pediatric obesity diagnosis over time that correlates with BMI measurements recorded in participants' adult EHRs and those physical measurements taken at enrollment in the research program.

DISCUSSION

We highlight the availability of retrospective pediatric EHR data for nearly 20 000 All of Us participants. These data are relevant to current issues such as the rise in pediatric obesity.

CONCLUSION

All of Us contains a rich resource of retrospective pediatric EHR data to accelerate pediatric research studies.

Application of Surgical Decision Model for Patients With Childhood Cataract: A Study Based on Real World Data

Reliable validated methods are necessary to verify the performance of diagnosis and therapy-assisted models in clinical practice. However, some validated results have research bias and may not reflect the results of real-world application. In addition, the conduct of clinical trials has executive risks for the indeterminate effectiveness of models and it is challenging to finish validated clinical trials of rare diseases. Real world data (RWD) can probably solve this problem. In our study, we collected RWD from 251 patients with a rare disease, childhood cataract (CC) and conducted a retrospective study to validate the CC surgical decision model. The consistency of the real surgical type and recommended surgical type was 94.16%. In the cataract extraction (CE) group, the model recommended the same surgical type for 84.48% of eyes, but the model advised conducting cataract extraction and primary intraocular lens implantation (CE + IOL) surgery in 15.52% of eyes, which was different from the real-world choices. In the CE + IOL group, the model recommended the same surgical type for 100% of eyes. The real-recommended matched rates were 94.22% in the eyes of bilateral patients and 90.38% in the eyes of unilateral patients. Our study is the first to apply RWD to complete a retrospective study evaluating a clinical model, and the results indicate the availability and feasibility of applying RWD in model validation and serve guidance for intelligent model evaluation for rare diseases.

Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

BACKGROUND

Identifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, methodologies in pediatric drug safety have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to identify drug event patterns within observational databases for evaluating ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood.

RESULTS

Using simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child development stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages.

CONCLUSIONS

Our study underscores the opportunity for using population modeling techniques, which leverage drug event reporting across development stages, as biologically-inspired detection methods for evaluating ontogenic mechanisms.

Progress in Drug Development-Pediatric Dose Selection: Workshop Summary

The "Pediatric Dose Selection" workshop was held in October 2020 and sponsored by the U.S. Food and Drug Administration and the University of Maryland Center for Excellence in Regulatory Science and Innovation. A summary of the presentations in the context of pediatric drug development is summarized in this article.

Considerations for Drug Dosing in Premature Infants

In premature infants, effective and safe drug therapy depends on optimal dose selection and requires a thorough understanding of the underlying disease(s) of these fragile infants as well as the pharmacokinetics and pharmacodynamics of the drugs selected to treat their diseases. Differences in gestational and postnatal age or weight are the major determinants of the observed variability in drug disposition and effect in these infants. This article presents an outline on how to translate the results of a population pharmacokinetic/pharmacodynamic study into rational dosing regimens, and how physiologically based pharmacokinetic modeling, electronic health records, and the abundantly available data of vital functions of premature infants during their stay in the neonatal intensive care unit for evaluation of their pharmacotherapy can be used to tailor the most safe and effective dose in these infants.

Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction

BACKGROUND

Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.

OBJECTIVE

The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

METHODS

Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.

RESULTS

The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.

CONCLUSION

A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.

From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics

Purpose of review

Machine learning (ML), a branch of artificial intelligence, is influencing all fields in medicine, with an abundance of work describing its application to adult practice. ML in pediatrics is distinctly unique with clinical, technical, and ethical nuances limiting the direct translation of ML tools developed for adults to pediatric populations. To our knowledge, no work has yet focused on outlining the unique considerations that need to be taken into account when designing and implementing ML in pediatrics.

Recent findings

The nature of varying developmental stages and the prominence of family-centered care lead to vastly different data-generating processes in pediatrics. Data heterogeneity and a lack of high-quality pediatric databases further complicate ML research. In order to address some of these nuances, we provide a common pipeline for clinicians and computer scientists to use as a foundation for structuring ML projects, and a framework for the translation of a developed model into clinical practice in pediatrics. Throughout these pathways, we also highlight ethical and legal considerations that must be taken into account when working with pediatric populations and data.

Summary

Here, we describe a comprehensive outline of special considerations required of ML in pediatrics from project ideation to implementation. We hope this review can serve as a high-level guideline for ML scientists and clinicians alike to identify applications in the pediatric setting, generate effective ML solutions, and subsequently deliver them to patients, families, and providers.

Defining the Path Forward for Biomarkers to Address Unmet Needs in Inflammatory Bowel Diseases

Despite major advances in the inflammatory bowel diseases field, biomarkers to enable personalized and effective management are inadequate. Disease course and treatment response are highly variable, with some patients experiencing mild disease progression, whereas other patients experience severe or complicated disease. Periodic endoscopy is performed to assess disease activity; as a result, it takes months to ascertain whether a treatment is having a positive impact on disease progression. Minimally invasive biomarkers for prognosis of disease course, prediction of treatment response, monitoring of disease activity, and accurate diagnosis based on improved disease phenotyping and classification could improve outcomes and accelerate the development of novel therapeutics. Rapidly developing technologies have great potential in this regard; however, the discovery, validation, and qualification of biomarkers will require partnerships including academia, industry, funders, and regulators. The Crohn's & Colitis Foundation launched the IBD Biomarker Summit to bring together key stakeholders to identify and prioritize critical unmet needs; prioritize promising technologies and consortium approaches to address these needs; and propose harmonization approaches to improve comparability of data across studies. Here, we summarize the outcomes of the 2018 and 2019 meetings, including consensus-based unmet needs in the clinical and drug development context. We highlight ongoing consortium efforts and promising technologies with the potential to address these needs in the near term. Finally, we summarize actionable recommendations for harmonization, including data collection tools for improved consistency in disease phenotyping; standardization of informed consenting; and development of guidelines for sample management and assay validation. Taken together, these outcomes demonstrate that there is an exceptional alignment of priorities across stakeholders for a coordinated effort to address unmet needs of patients with inflammatory bowel diseases through biomarker science.

From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges

Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population. Conversely, real-world observational data, such as electronic health records (EHRs), contain large amounts of clinical information about heterogeneous patients and their response to treatments. In this paper, we introduce the main opportunities and challenges in using observational data for training machine learning methods to estimate individualized treatment effects and make treatment recommendations. We describe the modeling choices of the state-of-the-art machine learning methods for causal inference, developed for estimating treatment effects both in the cross-section and longitudinal settings. Additionally, we highlight future research directions that could lead to achieving the full potential of leveraging EHRs and machine learning for making individualized treatment recommendations. We also discuss how experimental data from RCTs and Pharmacometric and Quantitative Systems Pharmacology approaches can be used to not only improve machine learning methods, but also provide ways for validating them. These future research directions will require us to collaborate across the scientific disciplines to incorporate models based on RCTs and known disease processes, physiology, and pharmacology into these machine learning models based on EHRs to fully optimize the opportunity these data present.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.