Tailoring epilepsy treatment: personalized micro-physiological systems illuminate individual drug responses

Introduction

Approximately 50 million people worldwide have epilepsy, with many not achieving seizure freedom. Organ-on-chip technology, which mimics organ-level physiology, could revolutionize drug development for epilepsy by replacing animal models in preclinical studies. The authors' goal is to determine if customized micro-physiological systems can lead to tailored drug treatments for epileptic patients.

Materials and methods

A comprehensive literature search was conducted utilizing various databases, including PubMed, Ebscohost, Medline, and the National Library of Medicine, using a predetermined search strategy. The authors focused on articles that addressed the role of personalized micro-physiological systems in individual drug responses and articles that discussed different types of epilepsy, diagnosis, and current treatment options. Additionally, articles that explored the components and design considerations of micro-physiological systems were reviewed to identify challenges and opportunities in drug development for challenging epilepsy cases.

Results

The micro-physiological system offers a more accurate and cost-effective alternative to traditional models for assessing drug effects, toxicities, and disease mechanisms. Nevertheless, designing patient-specific models presents critical considerations, including the integration of analytical biosensors and patient-derived cells, while addressing regulatory, material, and biological complexities. Material selection, standardization, integration of vascular systems, cost efficiency, real-time monitoring, and ethical considerations are also crucial to the successful use of this technology in drug development.

Conclusion

The future of organ-on-chip technology holds great promise, with the potential to integrate artificial intelligence and machine learning for personalized treatment of epileptic patients.

A regionally based precision medicine implementation initiative in North Africa:The PerMediNA consortium

Precision Medicine is being increasingly used in the developed world to improve health care. While several Precision Medicine (PM) initiatives have been launched worldwide, their implementations have proven to be more challenging particularly in low- and middle-income countries. To address this issue, the "Personalized Medicine in North Africa" initiative (PerMediNA) was launched in three North African countries namely Tunisia, Algeria and Morocco. PerMediNA is coordinated by Institut Pasteur de Tunis together with the French Ministry for Europe and Foreign Affairs, with the support of Institut Pasteur in France. The project is carried out along with Institut Pasteur d'Algérie and Institut Pasteur du Maroc in collaboration with national and international leading institutions in the field of PM including Institut Gustave Roussy in Paris. PerMediNA aims to assess the readiness level of PM implementation in North Africa, to strengthen PM infrastructure, to provide workforce training, to generate genomic data on North African populations, to implement cost effective, affordable and sustainable genetic testing for cancer patients and to inform policy makers on how to translate research knowledge into health products and services. Gender equity and involvement of young scientists in this implementation process are other key goals of the PerMediNA project. In this paper, we are describing PerMediNA as the first PM implementation initiative in North Africa. Such initiatives contribute significantly in shortening existing health disparities and inequities between developed and developing countries and accelerate access to innovative treatments for global health.

Machine Learning: A New Approach for Dose Individualization

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed. We have summarized the status of the study populations, predictive targets, and data sources for ML modeling, the selection of ML algorithms and features, and the evaluation and validation of their predictive performance. We also used the Prediction model Risk of Bias Assessment Tool (PROBAST) to assess the risk of bias of included studies. Currently, ML can be used for both a priori and a posteriori dose selection and optimization, and it can also assist the implementation of therapeutic drug monitoring. However, studies are mainly focused on drugs with narrow therapeutic windows, predominantly immunosuppressants (N = 23, 35.9%) and anti-infectives (N = 21, 32.8%), and there is currently only very limited attention for special populations, such as children (N = 22, 34.4%). Most studies showed poor methodological quality and a high risk of bias. The lack of external validation and clinical utility evaluation currently limits the further clinical implementation of ML for dose individualization. We therefore have proposed several ways to improve the clinical relevance of the studies and facilitate the translation of ML models into clinical practice.

Pediatric Cancer Drug Development: Leveraging Insights in Cancer Biology and the Evolving Regulatory Landscape to Address Challenges and Guide Further Progress

The discovery and development of anticancer drugs for pediatric patients have historically languished when compared to both past and recent activity in drug development for adult patients, notably the dramatic spike of targeted and immune-oncology therapies. The reasons for this difference are multifactorial. Recent changes in the regulatory landscape surrounding pediatric cancer drug development and the understanding that some pediatric cancers are driven by genetic perturbations that also drive disparate adult cancers afford new opportunities. The unique cancer-initiating events and dependencies of many pediatric cancers, however, require additional pediatric-specific strategies. Research efforts to unravel the underlying biology of pediatric cancers, innovative clinical trial designs, model-informed drug development, extrapolation from adult data, addressing the unique considerations in pediatric patients, and use of pediatric appropriate formulations, should all be considered for efficient development and dosage optimization of anticancer drugs for pediatric patients.

Reinforcement Learning and PK-PD Models Integration to Personalize the Adaptive Dosing Protocol of Erdafitinib in Patients with Metastatic Urothelial Carcinoma

The integration of pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulations with artificial intelligence/machine learning algorithms is one of the most attractive areas of the pharmacometric research. These hybrid techniques are currently under investigation to perform several tasks, among which precision dosing. In this scenario, this paper presents and evaluates a new framework embedding PK-PD models into a reinforcement learning (RL) algorithm, Q-learning (QL), to personalize pharmacological treatment. Each patient is represented with a set of PK-PD parameters and has a personal QL agent which optimizes the individual treatment. In the training phase, leveraging PK-PD simulations, the QL agent assesses different actions, defined consistently with the clinical knowledge to consider only plausible dose-adjustments, in order to find the optimal rules. The proposed framework was evaluated to optimize the erdafitinib treatment in patients with metastatic urothelial carcinoma. This drug was approved by the US Food and Drug Administration (FDA) with a dose-adaptive protocol based on monitoring the levels of serum phosphate, which represent a biomarker of both treatment efficacy and toxicity. To evaluate the flexibility of the methodology, a heterogeneous virtual population of 141 patients was generated using an erdafitinib population PK (PopPK)-PD literature model. For each patient, treatment response was simulated by using both QL-optimized protocol and the clinical one. QL agents outperform the approved dose-adaptive rules, increasing more than 10% the efficacy and the safety of treatment at each end point. Results confirm the great potentialities of the integration of PopPK-PD models and RL algorithms to optimize precision dosing tasks.

The Right Dose: Results of a Patient Advocate-Led Survey of Individuals With Metastatic Breast Cancer Regarding Treatment-Related Side Effects and Views About Dosage Assessment to Optimize Quality of Life

PURPOSE

Although patients with metastatic breast cancer (MBC) have been living longer with the advent of more effective treatments such as targeted therapy and immunotherapy, the disease remains incurable, and most patients will undergo therapy indefinitely. When beginning therapy, patients are typically prescribed dose often based upon the maximum tolerated dose identified in phase I clinical trials. However, patients' perspectives about tolerability and willingness to discuss individualized dosing of drugs upon initiation of a new regimen and throughout the course of treatment have not been comprehensively evaluated.

METHODS

Patient advocates and medical oncologists from the Patient-Centered Dosing Initiative (PCDI) developed a survey to ascertain the prevalence and severity of MBC patients' treatment-related side effects, the level of patient-physician communication, mitigation strategies, perception about the relative efficacy of higher versus lower doses, and willingness to discuss alternative dosing. The PCDI distributed the anonymous confidential online survey in August 2020 to individuals with self-reported MBC.

RESULTS

One thousand and two hundred twenty-one patients with MBC completed the survey. 86.1% (n = 1,051) reported experiencing at least one significant treatment-related side effect, and of these, 20.3% (n = 213) visited the emergency room/hospital and 43.2% (n = 454) missed at least one treatment. Nearly all patients with side effects (97.6%, n = 1,026) informed their doctor and 81.7% (n = 838) received assistance. Of the 556 patients given a dose reduction for side-effect mitigation, 82.6% (n = 459) reported relief. Notably, majority of patients (53.3%, n = 651) do not believe that higher dose is always more effective than lower dose, and 92.3% (n = 1,127) would be willing to discuss flexible dosing options with their physicians based upon personal characteristics to optimize quality of life.

CONCLUSION

Given that the majority of patients with MBC experienced at least one substantial treatment-related side effect and most patients given a dose reduction reported improvement, innovative dosage-related strategies are warranted to sustain and improve patients' well-being. Patient-physician discussions in which the patient's unique attributes and circumstances are assessed upon initiation of new treatment and throughout the course of therapy may facilitate the identification of the most favorable dose for each patient, and the majority of patients would be receptive to this approach.

Dose optimization of an adjuvanted peptide-based personalized neoantigen melanoma vaccine

The advancements in next-generation sequencing have made it possible to effectively detect somatic mutations, which has led to the development of personalized neoantigen cancer vaccines that are tailored to the unique variants found in a patient's cancer. These vaccines can provide significant clinical benefit by leveraging the patient's immune response to eliminate malignant cells. However, determining the optimal vaccine dose for each patient is a challenge due to the heterogeneity of tumors. To address this challenge, we formulate a mathematical dose optimization problem based on a previous mathematical model that encompasses the immune response cascade produced by the vaccine in a patient. We propose an optimization approach to identify the optimal personalized vaccine doses, considering a fixed vaccination schedule, while simultaneously minimizing the overall number of tumor and activated T cells. To validate our approach, we perform in silico experiments on six real-world clinical trial patients with advanced melanoma. We compare the results of applying an optimal vaccine dose to those of a suboptimal dose (the dose used in the clinical trial and its deviations). Our simulations reveal that an optimal vaccine regimen of higher initial doses and lower final doses may lead to a reduction in tumor size for certain patients. Our mathematical dose optimization offers a promising approach to determining an optimal vaccine dose for each patient and improving clinical outcomes.

Pharmacogenomics - a minor rather than major force in clinical medicine

INTRODUCTION

Pharmacogenomics (PGx) is touted as essential for the future of precision medicine. But the opportunity cost of PGx from the prescribers' perspective is rarely considered. The aim of this article is to critique PGx-guided prescribing using clinical pharmacology principles so that important cases for PGx testing are not missed by doctors responsible for therapeutic decision making.

AREAS COVERED

Three categories of PGx and their limitations are outlined - exposure PGx, response PGx, and immune-mediated safety PGx. Clinical pharmacology reasons are given for the narrow scope of PGx-guided prescribing apart from a few medical specialties. Clinical problems for doctors that may arise from PGx are then explained, including mismatch between patients' expectations of PGx testing and the benefits or answers it provides.

EXPERT OPINION

Contrary to popular opinion, PGx is unlikely to become the cornerstone of precision medicine. Sound clinical pharmacology reasons explain why PGx-guided prescribing is unnecessary for most drugs. Pharmacogenomics is important for niche areas of prescribing but has limited clinical utility more broadly. The opportunity cost of PGx-guided prescribing is currently too great for most doctors.

Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Present article for the first time performed a bibliometric analysis to clarify research status and focus from 2010 to 2021. In the analysis, a total of 2,312 relevant literature were searched and retrieved from the Web of Science Core Collection database. We conducted a bibliometric analysis and further visualization. During that time, exponentially growing annual publication and our model have shown a flourishing research prospect. Annual citation reached the peak in 2017. Researchers from United States and China have produced most of the relevant literature and strongest partnership between them. Medical image analysis and Nature appeared to bring more attention to the public. The computer-aided diagnosis, precision medicine, and survival prediction were the focus of research, reflecting the development trend at that period. ML did make a big difference in lung cancer research in the past decade.

Beyond Population-Level Targets for Drug Concentrations: Precision Dosing Needs Individual-Level Targets that Include Superior Biomarkers of Drug Responses

The purpose of precision dosing is to increase the chances of therapeutic success in individual patients. This is achieved in practice by adjusting doses to reach precision dosing targets determined previously in relevant populations, ideally with robust supportive evidence showing improved clinical outcomes compared with standard dosing. But is this implicit assumption of translatable population-level precision dosing targets correct and the best for all patients? In this review, the types of precision dosing targets and how they are determined are outlined, problems with the translatability of these targets to individual patients are identified, and ways forward to address these challengers are proposed. Achieving improved clinical outcomes to support precision dosing over standard dosing is currently hampered by applying population-level targets to all patients. Just as "one-dose-fits-all" may be an inappropriate philosophy for drug treatment overall, a "one-target-fits-all" philosophy may limit the broad clinical benefits of precision dosing. Defining individual-level precision dosing targets may be needed for greatest therapeutic success. Superior future precision dosing targets will integrate several biomarkers that together account for the multiple sources of drug response variability.

The Utility of Mixed Effects Models in the Evaluation of Complex Genomic Traits In Vitro

In pharmacogenomic studies, the use of human liver microsomes as a model system to evaluate the impact of complex genomic traits (i.e., linkage-disequilibrium patterns, coding, and non-coding variation, etc.) on efficiency of drug metabolism is challenging. To accurately predict the true effect size of genomic traits requires large richly sampled datasets representative of the study population. Moreover, the acquisition of this data can be labor-intensive if the study design or bioanalytical methods are not high throughput, and it is potentially unfeasible if the abundance of sample needed for experiments is limited. To overcome these challenges, we developed a novel strategic approach using non-linear mixed effects models (NLME) to determine enzyme kinetic parameters for individual liver specimens using sparse data. This method can facilitate evaluation of the impact that complex genomic traits have on the metabolism of xenobiotics in vitro when tissue and other resources are limited. In addition to facilitating the accrual of data, it allows for rigorous testing of covariates as sources of kinetic parameter variability. In this in silico study, we present a practical application of such an approach using previously published in vitro cytochrome P450 (CYP) 2D6 data and explore the impact of sparse sampling, and experimental error on known kinetic parameter estimates of CYP2D6 mediated formation of 4-hydroxy-atomoxetine in human liver microsomes. SIGNIFICANCE STATEMENT: This study presents a novel non-linear mixed effects model (NLME)-based framework for evaluating the impact of complex genomic traits on saturable processes described by a Michaelis-Menten kinetics in vitro using sparse data. The utility of this approach extends beyond gene variant associations, including determination of covariate effects on in vitro kinetic parameters and reduced demand for precious experimental material.

Virtual twin for healthcare management

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is "virtualized" for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Genotyping of Patients with Adverse Drug Reaction or Therapy Failure: Database Analysis of a Pharmacogenetics Case Series Study

Purpose

Pharmacogenetics (PGx) is an emerging aspect of personalized medicine with the potential to increase efficacy and safety of pharmacotherapy. However, PGx testing is still not routinely integrated into clinical practice. We conducted an observational case series study where PGx information from a commercially available panel test covering 30 genes was integrated into medication reviews. The aim of the study was to identify the drugs that are most frequently object of drug-gene-interactions (DGI) in the study population.

Patients and Methods

In out-patient and in-patient settings, we recruited 142 patients experiencing adverse drug reaction (ADR) and/or therapy failure (TF). Collected anonymized data from the individual patient was harmonized and transferred to a structured database.

Results

The majority of the patients had a main diagnosis of a mental or behavioral disorder (ICD-10: F, 61%), of musculoskeletal system and connective tissue diseases (ICD-10: M, 21%), and of the circulatory system (ICD-10: I, 11%). The number of prescribed medicines reached a median of 7 per person, resulting in a majority of patients with polypharmacy (≥5 prescribed medicines, 65%). In total, 559 suspected DGI were identified in 142 patients. After genetic testing, an association with at least one genetic variation was confirmed for 324 suspected DGI (58%) caused by 64 different drugs and 21 different genes in 141 patients. After 6 months, PGx-based medication adjustments were recorded for 62% of the study population, whereby differences were identified in subgroups.

Conclusion

The data analysis from this study provides valuable insights for the main focus of further research in the context of PGx. The results indicate that most of the selected patients in our sample represent suitable target groups for PGx panel testing in clinical practice, notably those taking drugs for mental or behavioral disorder, circulatory diseases, immunological diseases, pain-related diseases, and patients experiencing polypharmacy.

Targeted Therapy for Orofacial Pain: A Novel Perspective for Precision Medicine

Orofacial pain (OFP) is a dental specialty that includes the diagnosis, management and treatment of disorders of the jaw, mouth, face, head and neck. Evidence-based understanding is critical in effectively treating OFPs as the pathophysiology of these conditions is multifactorial. Since OFP impacts the quality of life of the affected individuals, treating patients successfully is of the utmost significance. Despite the therapeutic choices available, treating OFP is still quite challenging, owing to inter-patient variations. The emerging trends in precision medicine could probably lead us to a paradigm shift in effectively managing the untreatable long-standing pain conditions. Precision medicine is designed based on the patient's genetic profile to meet their needs. Several significant relationships have been discovered based on the genetics and genomics of pain in the past, and some of the notable targets are discussed in this review. The scope of this review is to discuss preclinical and clinical trials that include approaches used in targeted therapy for orofacial pain. Future developments in pain medicine should benefit from current trends in research into novel therapeutic approaches.

Polypharmacy and precision medicine

Precision medicine is an approach to maximise the effectiveness of disease treatment and prevention and minimise harm from medications by considering relevant demographic, clinical, genomic and environmental factors in making treatment decisions. Precision medicine is complex, even for decisions about single drugs for single diseases, as it requires expert consideration of multiple measurable factors that affect pharmacokinetics and pharmacodynamics, and many patient-specific variables. Given the increasing number of patients with multiple conditions and medications, there is a need to apply lessons learned from precision medicine in monotherapy and single disease management to optimise polypharmacy. However, precision medicine for optimisation of polypharmacy is particularly challenging because of the vast number of interacting factors that influence drug use and response. In this narrative review, we aim to provide and apply the latest research findings to achieve precision medicine in the context of polypharmacy. Specifically, this review aims to (1) summarise challenges in achieving precision medicine specific to polypharmacy; (2) synthesise the current approaches to precision medicine in polypharmacy; (3) provide a summary of the literature in the field of prediction of unknown drug-drug interactions (DDI) and (4) propose a novel approach to provide precision medicine for patients with polypharmacy. For our proposed model to be implemented in routine clinical practice, a comprehensive intervention bundle needs to be integrated into the electronic medical record using bioinformatic approaches on a wide range of data to predict the effects of polypharmacy regimens on an individual. In addition, clinicians need to be trained to interpret the results of data from sources including pharmacogenomic testing, DDI prediction and physiological-pharmacokinetic-pharmacodynamic modelling to inform their medication reviews. Future studies are needed to evaluate the efficacy of this model and to test generalisability so that it can be implemented at scale, aiming to improve outcomes in people with polypharmacy.

A metabolomics study: Could plasma metabolites be a guide for the prevention of tamsulosin side effects?

BACKGROUND

The understanding of precision medicine, which aims for high efficacy and low toxicity in treatments, has gained more importance with omics technologies. In this study, it was aimed to reach new suggestions for low-toxicity treatment by clarifying the relationship between tamsulosin side effects and metabolome profiles.

MATERIALS AND METHODS

Plasma samples of control and tamsulosin-treated rats were analyzed by LC-Q-TOF/MS/MS. MS/MS data was processed in XCMS software for the identification of metabolite and metabolic pathway analysis. Data were classified with MATLAB 2019b for multivariate data analysis. 34m/z values were found to be significantly different between the drug and control groups (P≤0.01 and fold analysis≥1.5) and identified by comparing METLIN and HMDB databases.

RESULTS

According to multivariate data analysis, α-Linolenic Acid, Thiamine, Retinoic acid, 1.25-Dihydroxyvitamin D3-26.23-Lactone, L-Glutamine, L-Serine, Retinaldehyde, Sphingosine 1-phosphate, L-Lysine, 23S.25-Dihydroxyvitamin D3, Sphinganine, L-Cysteine, Uridine 5'-diphosphate, Calcidiol, L-Tryptophan, L-Alanine levels changed significantly compared to the control group. Differences in the metabolisms of Retinol, Sphingolipid, Alanine-Aspartate-Glutamate, Glutathione, Fatty Acid, Tryptophan, and biosynthesis of Aminoacyl-tRNA, and Unsaturated Fatty Acid have been successfully demonstrated by metabolic pathway analysis. According to our study, vitamin A and D supplements can be recommended to prevent side effects such as asthenia, rhinitis, nasal congestion, dizziness and IFIS in the treatment of tamsulosin. Alteration of aminoacyl-tRNA biosynthesis and sphingolipid metabolism pathways during tamsulosin treatment is effective in the occurrence of nasal congestion.

CONCLUSIONS

Our study provides important information for tamsulosin therapy with high efficacy and low side effects in precision medicine.

Patient-centered dosing: oncologists' perspectives about treatment-related side effects and individualized dosing for patients with metastatic breast cancer (MBC)

PURPOSE

Although metastatic breast cancer (MBC) is treatable, it is not curable and most patients remain on treatment indefinitely. While oncologists commonly prescribe the recommended starting dose (RSD) from the FDA-approved label, patient tolerance may differ from that seen in clinical trials. We report on a survey of medical oncologists' perspectives about treatment-related toxicity and willingness to discuss flexible dosing with patients.

METHODS

We disseminated a confidential survey via social media/email in Spring 2021. Eligible respondents needed to be US-based medical oncologists with experience treating patients with MBC.

RESULTS

Of 131 responses, 119 were eligible. Physicians estimated that 47% of their patients reported distressing treatment-related side effects; of these, 15% visited the Emergency Room/hospital and 37% missed treatment. 74% (n = 87) of doctors reported improvement of patient symptoms after dose reduction. 87% (n = 104) indicated that they had ever, if appropriate, initiated treatment at lower doses. Most (85%, n = 101) respondents did not believe that the RSD is always more effective than a lower dose and 97% (n = 115) were willing to discuss individualized dosing with patients.

CONCLUSION

Treatment-related side effects are prevalent among patients with MBC, resulting in missed treatments and acute care visits. To help patients tolerate treatment, oncologists may decrease initial and/or subsequent doses. The majority of oncologists reject the premise that a higher dose is always superior and are willing to discuss individualized dosing with patients. Given potential improvements regarding quality of life and clinical care, dose modifications should be part of routine shared decision-making between patients and oncologists.

Model enhanced reinforcement learning to enable precision dosing: A theoretical case study with dosing of propofol

Extending the potential of precision dosing requires evaluating methodologies offering more flexibility and higher degree of personalization. Reinforcement learning (RL) holds promise in its ability to integrate multidimensional data in an adaptive process built toward efficient decision making centered on sustainable value creation. For general anesthesia in intensive care units, RL is applied and automatically adjusts dosing through monitoring of patient's consciousness. We further explore the problem of optimal control of anesthesia with propofol by combining RL with state-of-the-art tools used to inform dosing in drug development. In particular, we used pharmacokinetic-pharmacodynamic (PK-PD) modeling as a simulation engine to generate experience from dosing scenarios, which cannot be tested experimentally. Through simulations, we show that, when learning from retrospective trial data, more than 100 patients are needed to reach an accuracy within the range of what is achieved with a standard dosing solution. However, embedding a model of drug effect within the RL algorithm improves accuracy by reducing errors to target by 90% through learning to take dosing actions maximizing long-term benefit. Data residual variability impacts accuracy while the algorithm efficiently coped with up to 50% interindividual variability in the PK and 25% in the PD model's parameters. We illustrate how extending the state definition of the RL agent with meaningful variables is key to achieve high accuracy of optimal dosing policy. These results suggest that RL constitutes an attractive approach for precision dosing when rich data are available or when complemented with synthetic data from model-based tools used in model-informed drug development.

Altered pharmacology and toxicology during ageing: implications for lung disease

PURPOSE OF REVIEW

Drug use in elderly people is high compared to younger people. Simultaneously, elderly are at greater risk when exposed to environmental substances. It is puzzling therefore, that ageing, as a variable in pharmacological and toxicological processes is not investigated in more depth. Moreover, recent data suggest that molecular manifestations of the ageing process also hallmark the pathogenesis of chronic lung diseases, which may impact pharmacology and toxicology.

RECENT FINDINGS

In particular, absorption, distribution, metabolism and excretion (ADME) processes of drugs and toxins alter because of ageing. Polypharmacy, which is quite usual with increasing age, increases the risk of drug-drug interactions. Individual differences in combination of drugs use in conjunction with individual variations in drug metabolizing enzymes can influence lung function.

SUMMARY

Exploring exposure throughout life (i.e. during ageing) to potential triggers, including polypharmacy, may avoid lung disease or unexplained cases of lung damage. Understanding of the ageing process further unravels critical features of chronic lung disease and helps to define new protective targets and therapies. Optimizing resilience can be key in pharmacology and toxicology and helps in maintaining healthy lungs for a longer period.

Cost-Effectiveness of Pharmacogenomics-Guided Prescribing to Prevent Gene-Drug-Related Deaths: A Decision-Analytic Model

Aim: Prospective studies support the clinical impact of pharmacogenomics (PGx)-guided prescribing to reduce severe and potentially fatal adverse effects. Drug-gene interactions (DGIs) preventing potential drug-related deaths have been categorized as “essential” by the Dutch Pharmacogenetics Working Group (DPWG). The collective clinical impact and cost-effectiveness of this sub-set is yet undetermined. Therefore, we aim to assess impact and cost-effectiveness of “essential” PGx tests for prevention of gene-drug-related deaths, when adopted nation-wide.

Methods: We used a decision-analytic model to quantify the number and cost per gene-drug-related death prevented, from a 1-year Dutch healthcare perspective. The modelled intervention is a single gene PGx-test for CYP2C19, DPYD, TPMT or UGT1A1 to guide prescribing based on the DPWG recommendations among patients in the Netherlands initiating interacting drugs (clopidogrel, capecitabine, systemic fluorouracil, azathioprine, mercaptopurine, tioguanine or irinotecan).

Results: For 148,128 patients initiating one of seven drugs in a given year, costs for PGx-testing, interpretation, and drugs would increase by €21.4 million. Of these drug initiators, 35,762 (24.1%) would require an alternative dose or drug. PGx-guided prescribing would relatively reduce gene-drug related mortality by 10.6% (range per DGI: 8.1–14.5%) and prevent 419 (0.3% of initiators) deaths a year. Cost-effectiveness is estimated at €51,000 per prevented gene-drug-related death (range per DGI: €-752,000–€633,000).

Conclusion: Adoption of PGx-guided prescribing for “essential” DGIs potentially saves the lives of 0.3% of drug initiators, at reasonable costs.

Open Structural Data in Precision Medicine

Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. Computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalized pharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Recent advancements in clearance monitoring of monoclonal antibodies in patients with inflammatory bowel diseases

INTRODUCTION

Less than 50% of patients with inflammatory bowel diseases (IBD) receiving monoclonal antibody (mAb) therapy achieve endoscopic remission. Poor outcomes may indicate a need for dose optimization. During therapeutic drug monitoring (TDM), drug concentrations are measured, and when found too low, dosage regimen escalations are performed. To date, benefits of TDM of mAbs in patients with IBD are uncertain.

AREAS COVERED

This review presents an overview of what clearance monitoring is, how it can be performed, and why and when it may be valuable in treating patients with IBD. Virtual patients were used for illustration. A literature search was performed to summarize current evidence for clearance monitoring in IBD and other disease settings.

EXPERT OPINION

During clearance monitoring, mAb clearance is calculated and monitored over time. Higher mAb clearance in patients with IBD has been associated with higher target load (target-mediated drug disposition), protein-losing enteropathy (fecal drug loss), and immunogenicity. Although not prospectively confirmed, clearance monitoring might facilitate identification of (yet) asymptomatic disease flares or presence of (yet) undetectable anti-drug antibodies. Furthermore, clearance monitoring may be used to predict treatment outcomes. Whether dosage regimen adjustments can modify the clearance time course and the treatment outcome is to be determined.

Personalised, Rational, Efficacy-Driven Cancer Drug Dosing via an Artificial Intelligence SystEm (PRECISE): A Protocol for the PRECISE CURATE.AI Pilot Clinical Trial

Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations.

Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology.

Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals.

Trial registration number: NCT04522284

Perception of personalized medicine, pharmacogenomics, and genetic testing among undergraduates in Hong Kong

BACKGROUND

The global development and advancement of genomic medicine in the recent decade has accelerated the implementation of personalized medicine (PM) and pharmacogenomics (PGx) into clinical practice, while catalyzing the emergence of genetic testing (GT) with relevant ethical, legal, and social implications (ELSI).

RESULTS

The perception of university undergraduates with regards to PM and PGx was investigated, and 80% of undergraduates valued PM as a promising healthcare model with 66% indicating awareness of personal genome testing companies. When asked about the curriculum design towards PM and PGx, compared to undergraduates in non-medically related curriculum, those studying in medically related curriculum had an adjusted 7.2 odds of perceiving that their curriculum was well-designed for learning PGx (95% CI 3.6-14.6) and a 3.7 odds of perceiving that PGx was important in their study (95% CI 2.0-6.8). Despite this, only 16% of medically related curriculum undergraduates would consider embarking on future education on PM. When asked about their perceptions on GT, 60% rated their genetic knowledge as "School Biology" level or below while 76% would consider undergoing a genetic test. As for ELSI, 75% of undergraduates perceived that they were aware of ethical issues of GT in general, particularly on "Patient Privacy" (80%) and "Data Confidentiality" (68%). Undergraduates were also asked about their perceived reaction upon receiving an unfavorable result from GT, and over half of the participants perceived that they would feel "helpless or pessimistic" (56%), "inadequate or different" (59%), and "disadvantaged at job seeking" (59%), while older undergraduates had an adjusted 2.0 odds of holding the latter opinion (95% CI 1.1-3.5), compared to younger undergraduates.

CONCLUSION

Hong Kong undergraduates showed a high awareness of PM but insufficient genetic knowledge and low interest in pursuing a career towards PM. They were generally aware of ethical issues of GT and especially concerned about patient privacy and data confidentiality. There was a predominance of pessimistic views towards unfavorable testing results. This study calls for the attention to evaluate education and talent development on genomics, and update existing legal frameworks on genetic testing in Hong Kong.

Towards realizing the vision of precision medicine: AI based prediction of clinical drug response

Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and combine a hybrid data/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). We constructed a model that successfully predicts clinical drug response [area under the curve (AUC) = 0.76] and show that even with limited sample size, integrating high-dimensional genetics data with clinical data can inform drug response prediction. After further validation on data collected from an independently conducted clinical study (AUC = 0.75), we extensively explore our model to gain insights into the determinants of drug response, and identify various clinical and genetic characteristics predisposing to poor response. Finally, we assess the potential impact of our model on clinical trial design and demonstrate that, by enriching for probable responders, significant reductions in clinical study sizes may be achieved. To our knowledge, our model represents the first retrospectively validated machine learning model linking drug mechanism of action and the genetic, clinical and demographic background in epilepsy patients to clinical drug response. Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.

Point-of-Care Therapeutic Drug Monitoring for Precision Dosing of Immunosuppressive Drugs

BACKGROUND

Immunosuppressive drugs (ISD) are an essential tool in the treatment of transplant rejection and immune-mediated diseases. Therapeutic drug monitoring (TDM) for determination of ISD concentrations in biological samples is an important instrument for dose personalization for improving efficacy while reducing side effects. While currently ISD concentration measurements are performed at specialized, centralized facilities, making the process complex and laborious for the patient, various innovative technical solutions have recently been proposed for bringing TDM to the point-of-care (POC).

CONTENT

In this review, we evaluate current ISD-TDM and its value, limitations, and proposed implementations. Then, we discuss the potential of POC-TDM in the era of personalized medicine, and provide an updated review on the unmet needs and available technological solutions for the development of POC-TDM devices for ISD monitoring. Finally, we provide concrete suggestions for the generation of a meaningful and more patient-centric process for ISD monitoring.

SUMMARY

POC-based ISD monitoring may improve clinical care by reducing turnaround time, by enabling more frequent measurements in order to obtain meaningful pharmacokinetic data (i.e., area under the curve) faster reaction in case of problems and by increasing patient convenience and compliance. The analysis of the ISD-TDM field prompts the evolution of POC testing toward the development of fully integrated platforms able to support clinical decision-making. We identify 4 major areas requiring careful combined implementation: patient usability, data meaningfulness, clinicians' acceptance, and cost-effectiveness.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Assessment of Pharmacist's Knowledge and Perception toward 3D Printing Technology as a Dispensing Method for Personalized Medicine and the Readiness for Implementation

The main user of three dimensional (3D) printing for drug dispensing will be the hospital pharmacist. Yet despite the tremendous amount of research and industrial initiatives, there is no evaluation of the pharmacist’s knowledge and opinion of this technology. The present study aimed to assess knowledge and attitude among pharmacists about 3D printing technology as an innovative dispensing method for personalized medicine and the barriers to implementation in Saudi Arabia. We found that 53% of participants were aware of 3D printing technology in general, but only 14–16% of pharmacists were aware of the specific application of 3D printing in drug dispensing. Participants showed a positive perception regarding the concept of personalized medicine and that 3D printing could provide a promising solution to formulate and dispense personalized medicine in the pharmacy. It was also found that 67% of pharmacists were encouraged to adopt this new technology for drug dispensing, reflecting their willingness to learn new innovations. However, the technology cost, regulation, and the shortage of practicing pharmacists were also reported as the top barriers for implementation. Facilitating the implementation of this technology in the pharmacy practice will require a strategic plan in which pharmacists collaborate with regulatory bodies and 3D printing engineers to overcome challenges and barriers to implement such promising technology.

The pursuit of precision mitochondrial medicine: Harnessing preclinical cellular and animal models to optimize mitochondrial disease therapeutic discovery

Mitochondria share extensive evolutionary conservation across nearly all living species. This homology allows robust insights to be gained into pathophysiologic mechanisms and therapeutic targets for the heterogeneous class of primary mitochondrial diseases (PMDs) through the study of diverse in vitro cellular and in vivo animal models. Dramatic advances in genetic technologies, ranging from RNA interference to achieve graded knock-down of gene expression to CRISPR/Cas-based gene editing that yields a stable gene knock-out or targeted mutation knock-in, have enabled the ready establishment of mitochondrial disease models for a plethora of individual nuclear gene disorders. These models are complemented and extended by the use of pharmacologic inhibitor-based stressors to characterize variable degrees, onset, duration, and combinations of acute on chronic mitochondrial dysfunction in individual respiratory chain enzyme complexes or distinct biochemical pathways within mitochondria. Herein is described the rationale for, and progress made in, "therapeutic cross-training," a novel approach meant to improve the validity and rigor of experimental conclusions when testing therapies by studying treatment effects in multiple, evolutionarily-distinct species, including Caenorhabditis elegans (invertebrate, worm), Danio rerio (vertebrate, zebrafish), Mus musculus (mammal, mouse), and/or human patient primary fibroblast cell line models of PMD. The goal of these preclinical studies is to identify lead therapies from candidate molecules or library screens that consistently demonstrate efficacy, with minimal toxicity, in specific subtypes of mitochondrial disease. Conservation of in vitro and in vivo therapeutic effects of lead molecules across species has proven extensive, where molar concentrations found to be toxic or efficacious in one species are often consistent with therapeutic effects at similar doses seen in other mitochondrial disease models. Phenotypic outcome studies in all models are prioritized at the level of survival and function, to reflect the ultimate goal of developing highly potent therapies for human mitochondrial disease. Lead compounds that demonstrate significant benefit on gross phenotypes may be further scrutinized in these same models to decipher their cellular targets, mechanism(s), and detailed biochemical effects. High-throughput, automated technologic advances will be discussed that enable efficient, parallel screening in a diverse array of mitochondrial disease disorders and overarching subclasses of compounds, concentrations, libraries, and combinations. Overall, this therapeutic cross-training approach has proven valuable to identify compounds with optimal potency and safety profiles among major biochemical subtypes or specific genetic etiologies of mitochondrial disease. This approach further supports rational prioritization of lead compounds, target concentrations, and specific disease phenotypes, outcomes, and subgroups to optimally inform the design of clinical trials that test their efficacy in human mitochondrial disease subjects.

Precision Medicine: A New Paradigm in Therapeutics

A key goal of clinical care is to treat patients as individuals and to approach therapeutics in such a way that it has optimal efficacy and minimal toxicity. With swift technological advances, such as genomic sequencing and molecular targeted drug exploitation, the concept of precision medicine has been robustly promoted in recent years. Precision medicine endeavors to demarcate diseases using multiple data sources from genomics to digital health metrics in order to facilitate an individualized yet "evidence-based" decision regarding diagnostic and therapeutic approaches. In this way, therapeutics can be centered toward patients based on their molecular presentation rather than grouping them into broad categories with a "one size fits all" approach. This review article is aimed to provide a broad overview of the advent and emergence of precision medicine in view of its current implications.

A neuro evolutionary algorithm for patient calibrated prediction of survival in Glioblastoma patients

BACKGROUND AND OBJECTIVES

Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule.

METHODS

We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L₁₆ orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients.

RESULTS

Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.

Enlightenment about using TCM constitutions for individualized medicine and construction of Chinese-style precision medicine: research progress with TCM constitutions

TCM constitution is a new branch of TCM. It provides enlightenment on individualized medicine, including the development of new models of individualized research based on nine constitutions, the acquisition of comprehensive health information for individuals, and establishment of a consistent individualized diagnosis and treatment system. Further, we propose a Chinese-style "precision medicine" based on individualization using the TCM constitutions.

Personalised Medicine: implication and perspectives in the field of occupational health

"Personalised medicine" relies on identifying and integrating individual variability in genomic, biological, and physiological parameters, as well as in environmental and lifestyle factors, to define "individually" targeted disease prevention and treatment. Although innovative "omic" technologies supported the application of personalised medicine in clinical, oncological, and pharmacological settings, its role in occupational health practice and research is still in a developing phase. Occupational personalised approaches have been currently applied in experimental settings and in conditions of unpredictable risks, e.g.. war missions and space flights, where it is essential to avoid disease manifestations and therapy failure. However, a debate is necessary as to whether personalized medicine may be even more important to support a redefinition of the risk assessment processes taking into consideration the complex interaction between occupational and individual factors. Indeed, "omic" techniques can be helpful to understand the hazardous properties of the xenobiotics, dose-response relationships through a deeper elucidation of the exposure-disease pathways and internal doses of exposure. Overall, this may guide the adoption/implementation of primary preventive measures protective for the vast majority of the population, including most susceptible subgroups. However, the application of personalised medicine into occupational health requires overcoming some practical, ethical, legal, economical, and socio-political issues, particularly concerning the protection of privacy, and the risk of discrimination that the workers may experience. In this scenario, the concerted action of academic, industry, governmental, and stakeholder representatives should be encouraged to improve research aimed to guide effective and sustainable implementation of personalised medicine in occupational health fields.

Machine learning for precision medicine

Precision medicine is an emerging approach to clinical research and patient care that focuses on understanding and treating disease by integrating multi-modal or multi-omics data from an individual to make patient-tailored decisions. With the large and complex datasets generated using precision medicine diagnostic approaches, novel techniques to process and understand these complex data were needed. At the same time, computer science has progressed rapidly to develop techniques that enable the storage, processing, and analysis of these complex datasets, a feat that traditional statistics and early computing technologies could not accomplish. Machine learning, a branch of artificial intelligence, is a computer science methodology that aims to identify complex patterns in data that can be used to make predictions or classifications on new unseen data or for advanced exploratory data analysis. Machine learning analysis of precision medicine's multi-modal data allows for broad analysis of large datasets and ultimately a greater understanding of human health and disease. This review focuses on machine learning utilization for precision medicine's "big data", in the context of genetics, genomics, and beyond.

Site-specific delivery of green tea coated aluminium magnesium silicate beads and studies on their effect against chicken coccidiosis

In this report, the site-specific co-delivery of green tea/aluminium magnesium silicate (AMS) was reported and the specific target delivery was achieved orally. The new co-precipitation process was developed to synthesis the green tea/AMS hybrid complex and using energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy and Raman confirmed its successful synthesis. The blood biocompatibility of the green tea/AMS was tested using chicken blood, and the compound is safe up to 500 mg/ml. After mixed with hydroxypropyl methylcellulose phthalate, the oral beads were synthesised using a linking agent. The oral beads underwent different pH-based dissolution studies and the results indicated that the beads specifically dissolved in gastric pH (6.5). The pharmaco kinetic studies were performed to estimate the delivery kinetics. The results revealed that the beads underwent as per the Higuchi model. The anticoccidial effects of the beads were tested using chicken. The animal studies were performed in two different modes such as prophylactic treatment and active treatment after Eimeria species challenge. The results indicated that the prophylactic treatment with beads 100% protected the chicken and the active treatment with beads after the Eimeria challenge significantly protected against the intestinal damage and it also enhanced the anticoccidial effect.

Reinforcement learning for optimal scheduling of Glioblastoma treatment with Temozolomide

BACKGROUND

Glioblastoma multiforme (GBM) is the most frequent primary brain tumor in adults and Temozolomide (TMZ) is an effective chemotherapeutic agent for its treatment. In Silico models of GBM growth provide an appropriate foundation for analysis and comparison of different regimens. We propose a mathematical frame for patient specific design of optimal chemotherapy regimens for GBM patients.

METHODS

The proposed frame includes online interaction of a virtual GBM with an optimizing agent. Spatiotemporal dynamics of GBM growth and its response to TMZ are simulated with a three dimensional hybrid cellular automaton. Q learning is tailored to the virtual GBM for treatment optimization aimed at minimizing tumor size at the end of treatment course. Q learning consists of a learning agent that interacts with the virtual GBM. System state is affected by the agent decisions and the obtained rewards guide Q learning to the optimal schedule.

RESULTS

Computational results confirm that the optimal chemotherapy schedule depends on some patient specific parameters including body weight, tumor size and its position in the brain. Furthermore, the algorithm is used for scheduling 2100 mg of TMZ on a virtual GBM and the obtained schedule is to administer150 mg of TMZ every other day. The obtained schedule is compared to the standard 7/14 regimen and the results show that it is superior to the 7/14 regimen in minimizing tumor size.

CONCLUSION

The proposed frame is an appropriate decision support system for patient specific design of TMZ administration regimens on GBM patients. Also, since the obtained optimal schedule outperforms the standard 7/14 regimen, it is worthy of further clinical testing.

Molecular subtypes in canine hemangiosarcoma reveal similarities with human angiosarcoma

Angiosarcoma (AS) is a rare neoplasm with limited treatment options and a poor survival rate. Development of effective therapies is hindered by the rarity of this disease. Dogs spontaneously develop hemangiosarcoma (HSA), a common, histologically similar neoplasm. Metastatic disease occurs rapidly and despite chemotherapy, most dogs die several months after diagnosis. These features suggest that HSA might provide a tractable model to test experimental therapies in clinical trials. We previously reported whole exome sequencing of 20 HSA cases. Here we report development of a NGS targeted resequencing panel to detect driver mutations in HSA and other canine tumors. We validated the panel by resequencing the original 20 cases and sequenced 30 additional cases. Overall, we identified potential driver mutations in over 90% of the cases, including well-documented (in human cancers) oncogenic mutations in PIK3CA (46%), PTEN (6%), PLCG1(4%), and TP53 (66%), as well as previously undetected recurrent activating mutations in NRAS (24%). The driver role of these mutations is further demonstrated by augmented downstream signaling crucial to tumor growth. The recurrent, mutually exclusive mutation patterns suggest distinct molecular subtypes of HSA. Driver mutations in some subtypes closely resemble those seen in some AS cases, including NRAS, PLCG1, PIK3CA and TP53. Furthermore, activation of the MAPK and PI3K pathways appear to be key oncogenic mechanisms in both species. Together, these observations suggest that dogs with spontaneous HSA could serve as a useful model for testing the efficacy of targeted therapies, some of which could potentially be of therapeutic value in AS.

Model-Informed Artificial Intelligence: Reinforcement Learning for Precision Dosing

The availability of multidimensional data together with the development of modern techniques for data analysis represent an exceptional opportunity for clinical pharmacology. Data science-defined in this special issue as the novel approaches to the collection, aggregation, and analysis of data-can significantly contribute to characterize drug-response variability at the individual level, thus enabling clinical pharmacology to become a critical contributor to personalized healthcare through precision dosing. We propose a minireview of methodologies for achieving precision dosing with a focus on an artificial intelligence technique called reinforcement learning, which is currently used for individualizing dosing regimen in patients with life-threatening diseases. We highlight the interplay of such techniques with conventional pharmacokinetic/pharmacodynamic approaches and discuss applicability in drug research and early development.

Effect of ursodeoxycholic acid therapy due to pregnant intrahepatic cholestasis on chemerin and irisin levels

The purpose of the work was to assess changes in chemerin and irisin levels in women with diagnosed intrahepatic cholestasis of pregnant women treated with ursodeoxycholic acid. The study group consisted of 50 patients with diagnosed and confirmed intrahepatic cholestasis of pregnant women at 24-25 weeks of pregnancy treatment by ursodeoxycholic acid (UDCA). The study also included a group of 40 pregnant women, without concomitant intrahepatic cholestasis of pregnancy (ICP). In the pregnant ICP group, whole blood was collected 4 times: before the first dose of drug, 4 and 8 weeks after the first dose, and day after delivery. It was observed that statistically significant differences in the concentration of irisine occur between the time before starting treatment and the 8-week therapy and 1 day after delivery. The Pearson correlation analysis (r's) showed two statistically significant relationships (p < .05). The first of these can be found between the concentration of irisine and chemerin in the group of nonpregnant women and the second in the group of patients with intrahepatic pregnant cholestasis before the first dose of UDCA. A significant relationship between irisin and chemerin concentrations was confirmed in the group of pregnant ICP patients during UDCA acid therapy and among healthy pregnant women.

Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal.

Recent advances in taste transduction and signaling

In the last few years, single-cell profiling of taste cells and ganglion cells has advanced our understanding of transduction, encoding, and transmission of information from taste buds as relayed to the central nervous system. This review focuses on new knowledge from these molecular approaches and attempts to place this in the context of previous questions and findings in the field. The individual taste cells within a taste bud are molecularly specialized for detection of one of the primary taste qualities: salt, sour, sweet, umami, and bitter. Transduction and transmitter release mechanisms differ substantially for taste cells transducing sour (Type  III cells) compared with those transducing the qualities of sweet, umami, or bitter (Type II cells), although ultimately all transmission of taste relies on activation of purinergic P2X receptors on the afferent nerves. The ganglion cells providing innervation to the taste buds also appear divisible into functional and molecular subtypes, and each ganglion cell is primarily but not exclusively responsive to one taste quality.