Two heads are better than one: current landscape of integrating QSP and machine learning

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer ‘omics’ data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP ? ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices

Current knowledge, challenges and innovations in developmental pharmacology: A combined conect4children Expert Group and European Society for Developmental, Perinatal and Paediatric Pharmacology White Paper

Developmental pharmacology describes the impact of maturation on drug disposition (pharmacokinetics, PK) and drug effects (pharmacodynamics, PD) throughout the paediatric age range. This paper, written by a multidisciplinary group of experts, summarizes current knowledge, and provides suggestions to pharmaceutical companies, regulatory agencies and academicians on how to incorporate the latest knowledge regarding developmental pharmacology and innovative techniques into neonatal and paediatric drug development. Biological aspects of drug absorption, distribution, metabolism and excretion (ADME) throughout development are summarized. Although this area made enormous progress during the last two decades, remaining knowledge gaps were identified. Minimal risk and burden designs allow for optimally informative but minimally invasive PK sampling, while concomitant profiling of drug metabolites may provide additional insight in the unique PK behavior in children. Furthermore, developmental PD needs to be considered during drug development, which is illustrated by disease- and/or target organ-specific examples. Identifying and testing PD targets and effects in special populations, and application of age- and/or population-specific assessment tools are discussed. Drug development plans also need to incorporate innovative techniques like preclinical models to study therapeutic strategies, and shift from sequential enrollment of subgroups, to more rational designs. To stimulate appropriate research plans, illustrations of specific PK/PD-related as well as drug safety-related challenges during drug development are provided. The suggestions made in this joint paper of the Innovative Medicines Initiative conect4children Expert group on Developmental Pharmacology and the European Society for Developmental, Perinatal and Paediatric Pharmacology, should facilitate all those involved in drug development

Network and systems medicine: Position paper of the European Collaboration on Science and Technology action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management

Vocal Function Exercises for Normal Voice: The Effects of Varying Dosage

The primary purpose of this investigation was to explore the effects of variable doses of home practice Vocal Function Exercises (VFEs) on attainment of pre-established maximum phonation time (MPT) goals in individuals between the ages of 18 and 25 with normal voice. A secondary purpose was to monitor for potentially toxic effects of high doses of VFEs. Three experimental groups completed a six-week VFE protocol and practiced twice daily. The low dose group performed each exercise once, the traditional group twice, and the high dose group four times. Results indicated significant change in VFE MPT for all three groups and higher goal attainment in the high dose group. Low doses appear insufficient to produce substantial change in voice production. Acoustic MPT improved most in the traditional dosage group, which also exhibited best maintenance and best overall outcomes. No toxic effects in vocal fold condition or phonation were observed or measured secondary to high VFE exposure

Pharmacotherapy for neonatal seizures: current knowledge and future perspectives

Seizures are the most common neurological emergencies in the neonatal period and are associated with poor neurodevelopmental outcomes. Seizures affect up to five per 1000 term births and population-based studies suggest that they occur even more frequently in premature infants. Seizures are a sign of an underlying cerebral pathology, the most common of which is hypoxic-ischaemic encephalopathy in term infants. Due to a growing body of evidence that seizures exacerbate cerebral injury, effective diagnosis and treatment of neonatal seizures is of paramount importance to reduce long-term adverse outcomes. Electroencephalography is essential for the diagnosis of seizures in neonates due to their subtle clinical expression, non-specific neurological presentation and a high frequency of electro-clinical uncoupling in the neonatal period. Hypoxic-ischaemic encephalopathy may require neuroprotective therapeutic hypothermia, accompanying sedation with opioids, anticonvulsant drugs or a combination of all of these. The efficacy, safety, tolerability and pharmacokinetics of seven anticonvulsant drugs (phenobarbital, phenytoin, levetiracetam, lidocaine, midazolam, topiramate and bumetanide) are reviewed. This review is focused only on studies reporting electrographically confirmed seizures and highlights the knowledge gaps that exist in optimal treatment regimens for neonatal seizures. Randomised controlled trials are needed to establish a safe and effective treatment protocol for neonatal seizures

Mesocorticolimbic monoamine correlates of methamphetamine sensitization and motivation.

Methamphetamine (MA) is a highly addictive psychomotor stimulant, with life-time prevalence rates of abuse ranging from 5-10% world-wide. Yet, a paucity of research exists regarding MA addiction vulnerability/resiliency and neurobiological mediators of the transition to addiction that might occur upon repeated low-dose MA exposure, more characteristic of early drug use. As stimulant-elicited neuroplasticity within dopamine neurons innervating the nucleus accumbens (NAC) and prefrontal cortex (PFC) is theorized as central for addiction-related behavioral anomalies, we used a multi-disciplinary research approach in mice to examine the interactions between sub-toxic MA dosing, motivation for MA and mesocorticolimbic monoamines. Biochemical studies of C57BL/6J (B6) mice revealed short- (1 day), as well as longer-term (21 days), changes in extracellular dopamine, DAT and/or D2 receptors during withdrawal from 10, once daily, 2 mg/kg MA injections. Follow-up biochemical studies conducted in mice selectively bred for high vs. low MA drinking (respectively, MAHDR vs. MALDR mice), provided novel support for anomalies in mesocorticolimbic dopamine as a correlate of genetic vulnerability to high MA intake. Finally, neuropharmacological targeting of NAC dopamine in MA-treated B6 mice demonstrated a bi-directional regulation of MA-induced place-conditioning. These results extend extant literature for MA neurotoxicity by demonstrating that even subchronic exposure to relatively low MA doses are sufficient to elicit relatively long-lasting changes in mesocorticolimbic dopamine and that drug-induced or idiopathic anomalies in mesocorticolimbic dopamine may underpin vulnerability/resiliency to MA addiction

Bayesian Pharmacokinetic Models for Inference and Optimal Sequential Decision Making with Applications in Personalized Medicine

Patients can react to a drug differently just by virtue of being different people, and this between-patient variability in drug response is an obstacle to optimal treatment. Pharmacokinetic modelling offers one approach to studying drug response, often with covariate focused dose adjustment criteria being reported along side pharmacokinetic modelling. However, excess variation in concentrations continues to be reported despite the use of these criteria, bringing into question optimal dosing strategies for some drugs. This thesis provides methods for creating Bayesian pharmacokinetic models for two purposes: inference into the effects of covariates on concentrations and optimal sequential decision making for dose size. The thesis addresses three objectives: To compare existing approaches to fitting Bayesian models with recent advancements in pursuit of fitting population pharmacokinetic models, to develop a framework for evaluating the benefits of collecting additional information for use in personalization, and to demonstrate how academic personalized medicine researchers can use all data available to them to study effects of clinical variables on pharmacokinetics. To this end, the thesis makes three research contributions. First, a simulation study demonstrating that inferences using popular inference methods in pharmacokinetic research can lead to different and poorer calibrated decisions as compared to newer inference methods. The model presented in the simulation study was developed using a specific parameterization achieved through non-dimensionalization of the differential equation governing the mass transit of the drug and enables more reliable inference by sampling using Hamiltonian Monte Carlo as compared to a standard parameterization. Second, a unified framework for the development and simulation based evaluation of personalization based on pharmacokinetic modelling combined with dynamic treatment regimes. Lastly, a demonstration of how investigators can fit Bayesian pharmacokinetic models with the aim of accurate modelling of pharmacokinetics and exploration of novel variables using data from heterogeneous sources. These contributions provide methodologies do address two central goals of personalized medicine -- identification of factors driving between patient variability in drug response, and selection of an optimal dose -- and can enable a richer set of personalized decisions to be made

Electrophysiological characterisation of neuronal components of cold sensitivity

Aberrant cold sensitivity is apparent in several neuropathies of peripheral and central origin, and is poorly treated by currently available drugs. In an attempt to understand the mechanisms of cold evoked hyperalgesia and analgesia, these studies examined the dual pro- and anti-nociceptive roles of TRPM8, a cold temperature gated channel, and the role of calcium channels within cold sensitive pathways through a combination of in vivo electrophysiology, behavioural measures and gene ablation. Blocking TRPM8 with novel antagonists revealed lamina V/VI neuronal responses to innocuous and noxious cold stimulation were conserved in naïve rats. However, under neuropathic conditions inhibition of TRPM8 decreased neuronal responses to innocuous and noxious cold stimuli. This corresponded with an attenuation of behavioural hypersensitivity to innocuous cooling. Remarkably, systemically activating TRPM8 with a novel agonist resulted in identical neuronal and behavioural effects in neuropathic rats. Menthol is known to relieve various pain conditions as well as inducing hyperalgesia. Unlike in human subjects, menthol fails to induce central sensitisation in naïve rats, whereas in neuropathic rats topical menthol exerts some similar effects to the systemically dosed TRPM8 agonist. Gene ablation identifies a role of α2δ-1, an auxiliary calcium channel subunit, in cold and mechanical sensory pathways, likely dependent on impaired trafficking of calcium channels. Furthermore, α2δ-1 knockout mice exhibit a delay in the development of neuropathic like behaviours after injury. In neuropathic rats, systemic and spinal delivery of an activation state dependent Cav2 antagonist suppresses neuronal responses to mechanical stimuli but reveals no change in channel function within cold sensitive pathways. These findings expand the understanding of the neural basis of cold sensitivity and demonstrate TRPM8 is not essential to all forms of cold transduction in naïve rats, and that both inhibiting and activating TRPM8 have similar selective modality related inhibitory effects on cold transduction in neuropathic rats

Structuring the Unstructured: Unlocking pharmacokinetic data from journals with Natural Language Processing

The development of a new drug is an increasingly expensive and inefficient process. Many drug candidates are discarded due to pharmacokinetic (PK) complications detected at clinical phases. It is critical to accurately estimate the PK parameters of new drugs before being tested in humans since they will determine their efficacy and safety outcomes. Preclinical predictions of PK parameters are largely based on prior knowledge from other compounds, but much of this potentially valuable data is currently locked in the format of scientific papers. With an ever-increasing amount of scientific literature, automated systems are essential to exploit this resource efficiently. Developing text mining systems that can structure PK literature is critical to improving the drug development pipeline. This thesis studied the development and application of text mining resources to accelerate the curation of PK databases. Specifically, the development of novel corpora and suitable natural language processing architectures in the PK domain were addressed. The work presented focused on machine learning approaches that can model the high diversity of PK studies, parameter mentions, numerical measurements, units, and contextual information reported across the literature. Additionally, architectures and training approaches that could efficiently deal with the scarcity of annotated examples were explored. The chapters of this thesis tackle the development of suitable models and corpora to (1) retrieve PK documents, (2) recognise PK parameter mentions, (3) link PK entities to a knowledge base and (4) extract relations between parameter mentions, estimated measurements, units and other contextual information. Finally, the last chapter of this thesis studied the feasibility of the whole extraction pipeline to accelerate tasks in drug development research. The results from this thesis exhibited the potential of text mining approaches to automatically generate PK databases that can aid researchers in the field and ultimately accelerate the drug development pipeline. Additionally, the thesis presented contributions to biomedical natural language processing by developing suitable architectures and corpora for multiple tasks, tackling novel entities and relations within the PK domain