Perspective: Does personalized medicine hold the future for medicine?

Trends analysis and future study of the pharmaceutical industry field: a scoping review

OBJECTIVE

The main aim of this study was to explore and understand the emerging developments, changes, and patterns shaping the future of the pharmaceutical industry. The pharmaceutical industry is evolving rapidly due to scientific breakthroughs, societal changes, and technological advancements. Companies can adapt, and innovate to stay competitive and advance healthcare objectives by understanding the changes and patterns shaping the future.

METHODS

We conducted a thorough Scoping Review across PubMed, Embase, Web of Science, and Scopus, focusing on future trends in the pharmaceutical industry. Studies were carefully selected based on predefined criteria, ensuring a comprehensive and relevant collection.

RESULTS

We identified and selected 24 studies that met our predefined criteria from a pool of 3617 studies. These studies served as the foundation for a detailed exploration of trends within the pharmaceutical industry. This compilation offers a profound understanding of the diverse forces propelling change, including technological advancements, regulatory shifts, evolving market dynamics, and regional nuances.

CONCLUSIONS

This research highlights transformative trends such as artificial intelligence, 3D printing, blockchain technology, and digital integration revolutionizing drug discovery, development, manufacturing, and delivery. However, successful implementation hinges on addressing challenges through practical strategies, interdisciplinary collaborations, and region-specific considerations. Future research should explore implementation approaches, facilitating the effective translation of these innovations into tangible healthcare benefits. This forward-looking analysis empowers the pharmaceutical industry to strategically position itself for long-term success in addressing evolving global needs.

Engineering considerations of iPSC-based personalized medicine

Personalized medicine aims to provide tailored medical treatment that considers the clinical, genetic, and environmental characteristics of patients. iPSCs have attracted considerable attention in the field of personalized medicine; however, the inherent limitations of iPSCs prevent their widespread use in clinical applications. That is, it would be important to develop notable engineering strategies to overcome the current limitations of iPSCs. Such engineering approaches could lead to significant advances in iPSC-based personalized therapy by offering innovative solutions to existing challenges, from iPSC preparation to clinical applications. In this review, we summarize how engineering strategies have been used to advance iPSC-based personalized medicine by categorizing the development process into three distinctive steps: 1) the production of therapeutic iPSCs; 2) engineering of therapeutic iPSCs; and 3) clinical applications of engineered iPSCs. Specifically, we focus on engineering strategies and their implications for each step in the development of iPSC-based personalized medicine.

Data structuring may prevent ambiguity and improve personalized medical prognosis

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

A comparison of machine learning models for predicting urinary incontinence in men with localized prostate cancer

Introduction

Urinary incontinence (UI) is a common side effect of prostate cancer treatment, but in clinical practice, it is difficult to predict. Machine learning (ML) models have shown promising results in predicting outcomes, yet the lack of transparency in complex models known as "black-box" has made clinicians wary of relying on them in sensitive decisions. Therefore, finding a balance between accuracy and explainability is crucial for the implementation of ML models. The aim of this study was to employ three different ML classifiers to predict the probability of experiencing UI in men with localized prostate cancer 1-year and 2-year after treatment and compare their accuracy and explainability.

Methods

We used the ProZIB dataset from the Netherlands Comprehensive Cancer Organization (Integraal Kankercentrum Nederland; IKNL) which contained clinical, demographic, and PROM data of 964 patients from 65 Dutch hospitals. Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms were applied to predict (in)continence after prostate cancer treatment.

Results

All models have been externally validated according to the TRIPOD Type 3 guidelines and their performance was assessed by accuracy, sensitivity, specificity, and AUC. While all three models demonstrated similar performance, LR showed slightly better accuracy than RF and SVM in predicting the risk of UI one year after prostate cancer treatment, achieving an accuracy of 0.75, a sensitivity of 0.82, and an AUC of 0.79. All models for the 2-year outcome performed poorly in the validation set, with an accuracy of 0.6 for LR, 0.65 for RF, and 0.54 for SVM.

Conclusion

The outcomes of our study demonstrate the promise of using non-black box models, such as LR, to assist clinicians in recognizing high-risk patients and making informed treatment choices. The coefficients of the LR model show the importance of each feature in predicting results, and the generated nomogram provides an accessible illustration of how each feature impacts the predicted outcome. Additionally, the model's simplicity and interpretability make it a more appropriate option in scenarios where comprehending the model's predictions is essential.

Health technology assessment of whole genome sequencing in the diagnosis of genetic disorders: a scoping review of the literature

OBJECTIVE

The aim of this scoping review is to map the available evidence about the use of health technology assessment (HTA) in the assessment of whole genome sequencing (WGS).

METHODS

A scoping review methodology was adopted. The population, concept, and context framework was used to build up the research question and to establish the eligibility criteria. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews was adopted to implement a comprehensive search strategy. Evidence was retrieved from scientific databases and HTA organizations Web sites. Reports were classified as full HTA, mini-HTA, rapid reviews or other.

RESULTS

The search strategy identified seven reports. Five HTA organizations from five countries elaborated the reports: one full HTA, four rapid reviews, and two classified as others. The reports were mainly focused on the evaluation of the clinical utility and cost-effectiveness of genome-wide sequencing as well as informing policy questions by providing analyses of organizational and ethical considerations.

CONCLUSIONS

Few HTA organizations are drafting reports for WGS. It is essential to stimulate a critical reflection during the elaboration of HTA reports for WGS to steer choices of decision makers in the establishment of priorities for research and policy and reimbursement rates.

Combining explainable machine learning, demographic and multi-omic data to inform precision medicine strategies for inflammatory bowel disease

Inflammatory bowel diseases (IBDs), including ulcerative colitis and Crohn’s disease, affect several million individuals worldwide. These diseases are heterogeneous at the clinical, immunological and genetic levels and result from complex host and environmental interactions. Investigating drug efficacy for IBD can improve our understanding of why treatment response can vary between patients. We propose an explainable machine learning (ML) approach that combines bioinformatics and domain insight, to integrate multi-modal data and predict inter-patient variation in drug response. Using explanation of our models, we interpret the ML models’ predictions to infer unique combinations of important features associated with pharmacological responses obtained during preclinical testing of drug candidates in ex vivo patient-derived fresh tissues. Our inferred multi-modal features that are predictive of drug efficacy include multi-omic data (genomic and transcriptomic), demographic, medicinal and pharmacological data. Our aim is to understand variation in patient responses before a drug candidate moves forward to clinical trials. As a pharmacological measure of drug efficacy, we measured the reduction in the release of the inflammatory cytokine TNFα from the fresh IBD tissues in the presence/absence of test drugs. We initially explored the effects of a mitogen-activated protein kinase (MAPK) inhibitor; however, we later showed our approach can be applied to other targets, test drugs or mechanisms of interest. Our best model predicted TNFα levels from demographic, medicinal and genomic features with an error of only 4.98% on unseen patients. We incorporated transcriptomic data to validate insights from genomic features. Our results showed variations in drug effectiveness (measured by ex vivo assays) between patients that differed in gender, age or condition and linked new genetic polymorphisms to patient response variation to the anti-inflammatory treatment BIRB796 (Doramapimod). Our approach models IBD drug response while also identifying its most predictive features as part of a transparent ML precision medicine strategy.

Osteoarthritis, Corticosteroids and Role of CYP Genes in COVID-19 Patients: A Mini Review

Objectives of this review is to evaluate the role of cytochrome P450 gene polymorphisms in COVID-19 infected patients with pre-existing OA on corticosteroids. The purpose of this review is to analyze whether polymorphisms of Cytochrome p450 isoforms (CYP2C9 and CYP3A4) affect the dosage of steroids in OA patients in COVID-19 infected patients. This review may provide more therapeutic options; suggest a few guidelines which may be useful in managing COVID-19 patients with pre-existing osteoarthritis. The important role of corticosteroids in treating patients infected with COVID-19 with preexisting osteoarthritis, its influence on incidence of mortality or morbidity may be highlighted. The influence of CYP enzymes and their polymorphisms suggest safety of treatments as well as the possible need for the dosage adjustment or their discontinuation.

The Potential of Omics in Biological Dosimetry

Biological dosimetry is an internationally recognized method for quantifying and estimating radiation dose following suspected or verified excessive exposure to ionising radiation. In severe radiation accidents where a large number of people are potentially affected, it is possible to distinguish irradiated from non-irradiated people in order to initiate appropriate medical care if necessary. In addition to severe incidents caused by technical failure, environmental disasters, military actions, or criminal abuse, there are also radiation accidents in which only one or a few individuals are affected in the frame of occupational or medical exposure. The requirements for biological dosimetry are fundamentally different for these two scenarios. In particular, for large-scale radiation accidents, pre-screening methods are necessary to increase the throughput of samples for a rough first-dose categorization. The rapid development and increasing use of omics methods in research as well as in individual applications provides new opportunities for biological dosimetry. In addition to the discovery and search for new biomarkers, dosimetry assays based on omics technologies are becoming increasingly interesting and hold great potential, especially for large-scale dosimetry. In the following review, the different areas of biological dosimetry, the problems in finding suitable biomarkers, the current status of biomarker research based on omics, the potential applications of assays using omics technologies, and also the limitations for the different areas of biological dosimetry are discussed.

The Role of Soft Robotic Micromachines in the Future of Medical Devices and Personalized Medicine

Developments in medical device design result in advances in wearable technologies, minimally invasive surgical techniques, and patient-specific approaches to medicine. In this review, we analyze the trajectory of biomedical and engineering approaches to soft robotics for healthcare applications. We review current literature across spatial scales and biocompatibility, focusing on engineering done at the biotic-abiotic interface. From traditional techniques for robot design to advances in tunable material chemistry, we look broadly at the field for opportunities to advance healthcare solutions in the future. We present an extracellular matrix-based robotic actuator and propose how biomaterials and proteins may influence the future of medical device design.

Women's Reports of Barriers to and Facilitators of Oral Medication Adherence During Ovarian Stimulation: A Mixed Methods Pilot Study

Background:

Adherence to lifestyle modification recommendations remains problematic for women undergoing fertility treatment, raising concerns about the extent to which women adhere to prescribed medication regimens. Limited data have shown suboptimal oral medication adherence rates of 19% to 74%. The objective of this study was to explore what women perceive as barriers to and facilitators of oral medication adherence during fertility treatment cycles.

Methods:

An exploratory mixed methods pilot study was conducted among a sample of 30 women who were actively taking one to two cycles of letrozole or clomiphene citrate for ovarian stimulation in conjunction with intrauterine insemination cycles. Medication adherence barriers were measured using a 20-item survey. Medication adherence facilitators and personal experiences with fertility treatment were assessed with structured interviews. Medication adherence was assessed with electronic event monitoring.

Results:

The overall medication adherence median was 0.97 with a range of 0.75 to 1.00, and nine women (50%) demonstrated perfect adherence. The most commonly reported barriers were recently feeling sad, down, or blue (53%), and taking medication more than once per day (40%). Women with higher barrier scores had significantly lower medication adherence scores (p=0.02) compared to women with lower total barrier scores. Facilitators included using physical aides as reminders (60%) and establishing a daily routine (50%). No significant correlation was found between medication adherence scores and facilitators.

Conclusion:

The dynamic interplay between perceived barriers and facilitators and women’s medication-taking patterns could influence whether or not medication regimens are followed correctly.

Catalase Predicts In-Hospital Mortality after Out-of-Hospital Cardiac Arrest

The generation of harmful reactive oxygen species (ROS), including hydrogen peroxide, in out-of-hospital cardiac arrest (OHCA) survivors causes systemic ischemia/reperfusion injury that may lead to multiple organ dysfunction and mortality. We hypothesized that the antioxidant enzyme catalase may attenuate these pathophysiological processes after cardiac arrest. Therefore, we aimed to analyze the predictive value of catalase levels for mortality in OHCA survivors. In a prospective, single-center study, catalase levels were determined in OHCA survivors 48 h after the return of spontaneous circulation. Thirty-day mortality was defined as the study end point. A total of 96 OHCA survivors were enrolled, of whom 26% (n = 25) died within the first 30 days after OHCA. The median plasma intensity levels (log₂) of catalase were 8.25 (IQR 7.64–8.81). Plasma levels of catalase were found to be associated with mortality, with an adjusted HR of 2.13 (95% CI 1.07–4.23, p = 0.032). A Kaplan–Meier analysis showed a significant increase in 30-day mortality in patients with high catalase plasma levels compared to patients with low catalase levels (p = 0.012). High plasma levels of catalase are a strong and independent predictor for 30-day mortality in OHCA survivors. This indicates that ROS-dependent tissue damage is playing a crucial role in fatal outcomes of post-cardiac syndrome patients.

Impact of patients' gender on microvascular lower extremity reconstruction

The physiological differences between genders have significant implications for health and disease. With regard to microsurgery, results remain elusive as to whether male or female gender is an independent risk factor for free flap reconstruction. This study evaluated the impact of gender on outcomes of lower-extremity free-flap reconstructions. Within 7 years, 358 patients received 393 microvascular lower limb free flap reconstructions. The cases were divided into two groups according to patients' gender: male vs. female. Retrospective data analysis evaluated patients' demographics, perioperative details, surgical complications and flap outcomes over a 3-month follow-up period. Major and minor surgical complications, including total and partial flap loss, showed no significant differences between the investigated groups (p>.05). In addition, there was no significant difference with regard to the rate of surgical revision surgery, or the incidence of arterial and venous thrombosis (p>.05). Comparison of different flap types (fasciocutaneous ALT vs. gracilis muscle flaps) and type of anastomosis (end-to-end vs. end-to-side) also revealed no difference in outcomes in respect to gender. In conclusion, gender cannot be regarded as an independent risk factor for free flap reconstructions in patients with lower-extremity defects.

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person's variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

Nanomedicine: Photo-activated nanostructured titanium dioxide, as a promising anticancer agent

The multivariate condition of cancer disease has been approached in various ways, by the scientific community. Recent studies focus on individualized treatments, minimizing the undesirable consequences of the conventional methods, but the development of an alternative effective therapeutic scheme remains to be held. Nanomedicine could provide a solution, filling this gap, exploiting the unique properties of innovative nanostructured materials. Nanostructured titanium dioxide (TiO₂) has a variety of applications of daily routine and of advanced technology. Due to its biocompatibility, it has also a great number of biomedical applications. It is now clear that photo-excited TiO₂ nanoparticles, induce generation of pairs of electrons and holes which react with water and oxygen to yield reactive oxygen species (ROS) that have been proven to damage cancer cells, triggering controlled cellular processes. The aim of this review is to provide insights into the field of nanomedicine and particularly into the wide context of TiO₂-NP-mediated anticancer effect, shedding light on the achievements of nanotechnology and proposing this nanostructured material as a promising anticancer photosensitizer.

Use of Machine Learning and Artificial Intelligence to Drive Personalized Medicine Approaches for Spine Care

Personalized medicine is a new paradigm of healthcare in which interventions are based on individual patient characteristics rather than on "one-size-fits-all" guidelines. As epidemiological datasets continue to burgeon in size and complexity, powerful methods such as statistical machine learning and artificial intelligence (AI) become necessary to interpret and develop prognostic models from underlying data. Through such analysis, machine learning can be used to facilitate personalized medicine via its precise predictions. Additionally, other AI tools, such as natural language processing and computer vision, can play an instrumental part in personalizing the care provided to patients with spine disease. In the present report, we discuss the current strides made in incorporating AI into research on spine disease, especially traumatic spinal cord injury and degenerative spine disease. We describe studies using AI to build accurate prognostic models, extract important information from medical reports via natural language processing, and evaluate functional status in a granular manner using computer vision. Through a case illustration, we have demonstrated how these breakthroughs can facilitate an increased role for more personalized medicine and, thus, change the landscape of spine care.

Milestones in Personalized Medicine: From the Ancient Time to Nowadays-the Provocation of COVID-19

The first evidence of individual targeting medicine appeared in ancient times thousands of years ago. Various therapeutic approaches have been established since then. However, even nowadays, conventional therapies do not take into consideration individuals' idiosyncrasy and genetic make-up, failing thus to be effective in some cases. Over time, the necessity of a more precise and effective treatment resulted in the development of a scientific field currently known as “personalized medicine.” The numerous technological breakthroughs in this field have acknowledged personalized medicine as the next generation of diagnosis and treatment. Although personalized medicine has attracted a lot of attention the last years, there are still several obstacles hindering its application in clinical practice. These limitations have come to light recently, due to the COVID-19 pandemic. This review describes the “journey” of personalized medicine over time, emphasizing on important milestones achieved through time. Starting from the treatment of malaria, as a first more personalized therapeutic approach, it highlights the need of new diagnostic tools and therapeutic regimens based on individuals' genetic background. Furthermore, it aims at raising global awareness regarding the current limitations and the necessity of a personalized strategy to overpass healthcare problems and hence, the current crisis.

New agents in acute myeloid leukemia (AML)

Despite expanding knowledge in the molecular landscape of acute myeloid leukemia (AML) and an increasing understanding of leukemogenic pathways, little has changed in the treatment of AML in the last 40 years. Since introduction in the 1970s, combination chemotherapy consisting of anthracycline and cytarabine has been the mainstay of treatment, with major therapeutic advances based on improving supportive care rather than the introduction of novel therapeutics. Over the last decades, there have been extensive efforts to identify specific target mutations or pathways with the aim of improving clinical outcomes. Finally, after a prolonged wait, we are witnessing the next wave of AML treatment, characterized by a more “precise” and “personalized” understanding of the unique molecular or genetic mapping of individual patients. This new trend has since been further facilitated, with four new FDA approvals granted in 2017 in AML therapeutics. Currently, a total of eight targeted agents have been approved since 2017 (as of Jan. 2020). In this review, we will briefly discuss these newer agents in the context of their indication and the basis of their approval.

A novel mutation of indoleamine 2,3-dioxygenase 1 causes a rapid proteasomal degradation and compromises protein function

Indoleamine 2,3-dioxygenase 1 (IDO1) - the enzyme catalyzing the rate-limiting step of tryptophan catabolism along the kynurenine pathway - belongs to the class of inhibitory immune checkpoint molecules. Such regulators of the immune system are crucial for maintaining self-tolerance and thus, when properly working, preventing autoimmunity. A dysfunctional IDO1 has recently been associated with a specific single nucleotide polymorphism (SNP) and with the occurrence of autoimmune diabetes and multiple sclerosis. Many genetic alterations of IDO1 have been proposed being related with dysimmune disorders. However, the molecular and functional meaning of variations in IDO1 exomes as well as the promoter region remains a poorly explored field. In the present study, we identified a rare missense variant (rs751360195) at the IDO1 gene in a patient affected by coeliac disease, thyroiditis, and selective immunoglobulin A deficiency. Molecular and functional studies demonstrated that the substitution of lysine (K) at position 257 with a glutamic acid (E) results in an altered IDO1 protein that undergoes a rapid protein turnover. This genotype-to-phenotype relation is produced by peripheral blood mononuclear cells (PBMCs) of the patient bearing this variation and is associated with a specific phenotype (i.e., impaired tryptophan catabolism and defective mechanisms of immune tolerance). Thus decoding functional mutations of the IDO1 exome may provide clinically relevant information exploitable to personalize therapeutic interventions.

Pharmacogenomics at the center of precision medicine: challenges and perspective in an era of Big Data

Pharmacogenomics (PGx) is one of the core elements of personalized medicine. PGx information reduces the likelihood of adverse drug reactions and optimizes therapeutic efficacy. St Catherine Specialty Hospital in Zagreb/Zabok, Croatia has implemented a personalized patient approach using the RightMed^® Comprehensive PGx panel of 25 pharmacogenes plus Facor V Leiden, Factor II and MTHFR genes, which is interpreted by a special counseling team to offer the best quality of care. With the advent of significant technological advances comes another challenge: how can we harness the data to inform clinically actionable measures and how can we use it to develop better predictive risk models? We propose to apply the principles artificial intelligence to develop a medication optimization platform to prevent, manage and treat different diseases.

Efficacy and safety of duloxetine and Pregabalin in Iranian patients with diabetic peripheral neuropathic pain: a double-blind, randomized clinical trial

Purpose

Diabetic peripheral neuropathic pain (DPNP) is one of the most sufferings, disabling, and dominant complications of diabetes. Duloxetine (DLX) and Pregabalin (PGB) are among first-line therapy and the most prescribed drugs for DPNP relief. The effectiveness-risk profile of drugs may differ from region to region due to variations in genetic and health situation of populations. This study aims to evaluate the efficacy and safety of DLX and PGB in a sample of Iranian population with DPNP.

Methods

A double-blind, randomized clinical trial was conducted on 180 type-2 diabetic patients with DPNP≥40 mm according to Visual Analogue Scale (VAS), with other eligibility criteria throughout twelve weeks. We divided the patients randomly into two equal groups: DLX and PGB. Each patient received ten days placebo as a washout period, then blind capsules of DLX (group 1) or PGB (group 2). We assessed the efficacy and safety of drugs by VAS and recorded the Adverse Drug Reactions (ADRs) during the study.

Results

In the DLX group, sixty-six and the PGB group, seventy-eight patients completed the study. The intensity of patients’ pain was improved by both drugs significantly (p˂0.001), but there was no significant difference between the two groups. Average daily doses of DLX and PGB were 42.5 and 235.5 mg, respectively. In the DLX group, 74% of patients and the PGB group, 37% reported ADRs. The discontinuation rates due to ADRs were 19% and 7% correspondingly.

Conclusion

We found that in Iranian patients, the mean effective doses of these drugs are different in comparison with several other studies. Surprisingly intolerance and discontinuation of DLX in our patients were attributed to mild and severe Serotonin Syndrome, which had not much occurred in other studies. Accordingly, despite the same efficacy, PGB was better tolerated than DLX in our patients. Thus we would recommend PGB for DPNP treatment in Iranian patients.

Application of pharmacogenomics and bioinformatics to exemplify the utility of human ex vivo organoculture models in the field of precision medicine

Here we describe a collaboration between industry, the National Health Service (NHS) and academia that sought to demonstrate how early understanding of both pharmacology and genomics can improve strategies for the development of precision medicines. Diseased tissue ethically acquired from patients suffering from chronic obstructive pulmonary disease (COPD), was used to investigate inter-patient variability in drug efficacy using ex vivo organocultures of fresh lung tissue as the test system. The reduction in inflammatory cytokines in the presence of various test drugs was used as the measure of drug efficacy and the individual patient responses were then matched against genotype and microRNA profiles in an attempt to identify unique predictors of drug responsiveness. Our findings suggest that genetic variation in CYP2E1 and SMAD3 genes may partly explain the observed variation in drug response.

Randomized Trial of First-Line Behavioral Intervention to Reduce Need for Medication in Children with ADHD

A study conducted in an analogue summer treatment setting showed that when concurrently receiving behavioral intervention, many children with Attention-Deficit Hyperactivity Disorder (ADHD) did not need medication or maximized responsiveness at very low doses. The present study followed participants in that summer study into the subsequent school year to investigate whether the same pattern would extend to the natural school and home settings. There were 127 unmedicated children with ADHD between the ages of 5 and 13 who were randomly assigned to receive or not receive behavioral consultation (BC) at the start of the school year. Children were evaluated by teachers and parents each week to determine if central nervous system stimulant treatment was needed. Children who received BC were approximately half as likely those who did not (NoBC) to initiate medication use each week at school or home and used lower doses when medicated at school. This produced a 40% reduction in total methylphenidate exposure over the course of the school year. BC and NoBC groups did not significantly differ on end-of-year teacher or parent ratings of behavior, which were positive. Moreover, BC and NoBC groups did not significantly differ in cost of treatment; although children in the BC condition accrued additional costs via the BC, these costs were offset by the associated delay and reduction in medication use. Results add to a growing literature suggesting that the use of low-intensity behavioral intervention as a first-line treatment reduces or eliminates the need for medication in children with ADHD.

Allocating healthcare resources to genomic testing in Canada: latest evidence and current challenges

Precision medicine (PM) informed by next-generation sequencing (NGS) poses challenges for health technology assessment (HTA). To date, there has been limited reimbursement of genomic testing with NGS in Canada, particularly for whole-genome and whole-exome sequencing (WGS/WES). Through a structured literature review, we examine Canadian economic evidence and evidentiary challenges for the adoption of genomic testing. We searched Medline (PubMed) for published Canadian studies generating economic evidence for PM informed by NGS. Our search focused on studies examining the costs and/or value of NGS. We reviewed included studies and summarized results according to evaluation type, clinical context, NGS technology, and test strategy. We then grouped HTA challenges encountered by authors when evaluating NGS. Our review included twenty-five studies. To determine the economic impacts of NGS-informed PM in Canada, studies applied cost-effectiveness analysis (52%, n = 13), stated preference analysis (20%, n = 5), cost-consequence analysis (16%, n = 4), and healthcare resource utilization or costing analysis (12%, n = 3). NGS panels were the most common technology evaluated (n = 13), followed by WGS and/or WES (n = 8). The included studies highlighted multiple challenges when generating economic evidence, many of which remain unaddressed. Challenges were broadly related to (1) accounting for all NGS outcomes; (2) addressing uncertainty; and (3) improving consistency of economic approaches. Canadian studies are beginning to produce estimates of the economic impacts of NGS-informed PM, yet challenges for HTA remain. While solutions and real-world evidence are generated, lifecycle health technology management methods can be designed to better support resource allocation decisions for genomic testing in Canada.

100 Years of evolving gene-disease complexities and scientific debutants

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

Understanding Central Nervous System Effects of Deliriant Hallucinogenic Drugs through Experimental Animal Models

Hallucinogenic drugs potently alter human behavior and have a millennia-long history of use for medicinal and religious purposes. Interest is rapidly growing in their potential as CNS modulators and therapeutic agents for brain conditions. Antimuscarinic cholinergic drugs, such as atropine and scopolamine, induce characteristic hyperactivity and dream-like hallucinations and form a separate group of hallucinogens known as "deliriants". Although atropine and scopolamine are relatively well-studied drugs in cholinergic physiology, deliriants represent the least-studied class of hallucinogens in terms of their behavioral and neurological phenotypes. As such, novel approaches and new model organisms are needed to investigate the CNS effects of these compounds. Here, we comprehensively evaluate the preclinical effects of deliriant hallucinogens in various animal models, their mechanisms of action, and potential interplay with other signaling pathways. We also parallel experimental and clinical findings on deliriant agents and outline future directions of translational research in this field.

Current landscape of personalized medicine adoption and implementation in Southeast Asia

BACKGROUND

The emergence of personalized medicine (PM) has raised some tensions in healthcare systems. PM is expensive and health budgets are constrained - efficient healthcare delivery is therefore critical. Notwithstanding the cost, many countries have started to adopt this novel technology, including resource-limited Southeast Asia (SEA) countries. This study aimed to describe the status of PM adoption in SEA, highlight the challenges and to propose strategies for future development.

METHODS

The study included scoping review and key stakeholder interviews in four focus countries - Indonesia, Malaysia, Singapore, and Thailand. The current landscape of PM adoption was evaluated based on an assessment framework of six key themes - healthcare system, governance, access, awareness, implementation, and data. Six PM programs were evaluated for their financing and implementation mechanisms.

RESULTS

The findings revealed SEA has progressed in adopting PM especially Singapore and Thailand. A regional pharmacogenomics research network has been established. However, PM policies and programs vary significantly. As most PM programs are champion-driven and the available funding is limited, the current PM distribution has the potential to widen existing health disparities. Low PM awareness in the society and the absence of political support with financial investment are fundamental barriers. There is a clear need to broaden opportunities for critical discourse about PM especially for policymakers. Multi-stakeholder, multi-country strategies need to be prioritized in order to leverage resources and expertise.

CONCLUSIONS

Adopting PM remains in its infancy in SEA. To achieve an effective PM adoption, it is imperative to balance equity issues across diverse populations while improving efficiency in healthcare.

The SNPcurator: literature mining of enriched SNP-disease associations

The uniqueness of each human genetic structure motivated the shift from the current practice of medicine to a more tailored one. This personalized medicine revolution would not be possible today without the genetics data collected from genome-wide association studies (GWASs) that investigate the relation between different phenotypic traits and single-nucleotide polymorphisms (SNPs). The huge increase in the literature publication space imposes a challenge on the conventional manual curation process which is becoming more and more expensive. This research aims at automatically extracting SNP associations of any given disease and its reported statistical significance (P-value) and odd ratio as well as cohort information such as size and ethnicity. Our evaluation illustrates that SNPcurator was able to replicate a large number of SNP-disease associations that were also reported in the NHGRI-EBI Catalog of published GWASs. SNPcurator was also tested by eight external genetics experts, who queried the system to examine diseases of their choice, and was found to be efficient and satisfactory. We conclude that the text-mining-based system has a great potential for helping researchers and scientists, especially in their preliminary genetics research. SNPcurator is publicly available at http://snpcurator.science.uu.nl/.

Database URL: http://snpcurator.science.uu.nl/

Pharmacogenetics: An Important Part of Drug Development with A Focus on Its Application

Since the human genome project in 2003, the view of personalized medicine to improve diagnosis and cure diseases at the molecular level became more real. Sequencing the human genome brought some benefits in medicine such as early detection of diseases with a genetic predisposition, treating patients with rare diseases, the design of gene therapy and the understanding of pharmacogenetics in the metabolism of drugs. This review explains the concepts of pharmacogenetics, polymorphisms, mutations, variations, and alleles, and how this information has helped us better understand the metabolism of drugs. Multiple resources are presented to promote reducing the gap between scientists, physicians, and patients in understanding the use and benefits of pharmacogenetics. Some of the most common clinical examples of genetic variants and how pharmacogenetics was used to determine treatment options for patients having these variants were discussed. Finally, we evaluated some of the challenges of implementing pharmacogenetics in a clinical setting and proposed actions to be taken to make pharmacogenetics a standard diagnostic tool in personalized medicine.

A Simple Blood Test, Such as Complete Blood Count, Can Predict Calcification Grade of Abdominal Aortic Aneurysm

Objective

The pathogenesis of abdominal aortic aneurysm (AAA) is complex and different factors, including calcification, are linked to increased complications. This study was conducted in order to verify if classical risk factors for AAA and cell blood count parameter could help in the identification of calcification progression of the aneurysm.

Design

Risk factors were collected and cell blood count was performed in patients with AAA and patients were analyzed for the presence of aorta calcification using CT angiography.

Results

We found no association of calcification grade with risk factors for AAA but we found a strong association between MCV, MCH, and calcification grade. Instead, no association was found with the other parameter that we analyzed.

Conclusions

In this study, we demonstrate that biomarkers such as MCV and MCH could have potential important information about AAA calcification progression and could be useful to discriminate between those patients that should undergo a rapid imaging, thus allowing prompt initiation of treatment of suspicious patients that do not need imaging repetition.

Personalized Medicine: New Perspectives for the Diagnosis and the Treatment of Renal Diseases

The prevalence of renal diseases is rising and reaching 5-15% of the adult population. Renal damage is associated with disturbances of body homeostasis and the loss of equilibrium between exogenous and endogenous elements including drugs and metabolites. Studies indicate that renal diseases are influenced not only by environmental but also by genetic factors. In some cases the disease is caused by mutation in a single gene and at that time severity depends on the presence of one or two mutated alleles. In other cases, renal disease is associated with the presence of alteration within a gene or genes, but environmental factors are also necessary for the development of disease. Therefore, it seems that the analysis of genetic aspects should be a natural component of clinical and experimental studies. The goal of personalized medicine is to determine the right drug, for the right patient, at the right time. Whole-genome examinations may help to change the approach to the disease and the patient resulting in the creation of "personalized medicine" with new diagnostic and treatment strategies designed on the basis of genetic background of each individual. The identification of high-risk patients in pharmacogenomics analyses will help to avoid many unwarranted side effects while optimizing treatment efficacy for individual patients. Personalized therapies for kidney diseases are still at the preliminary stage mainly due to high costs of such analyses and the complex nature of human genome. This review will focus on several areas of interest: renal disease pathogenesis, diagnosis, treatment, rate of progression and the prediction of prognosis.

Circulating cell-free nucleic acids as potential biomarkers for sarcopenia: a step toward personalized medicine

Sarcopenia is an age-related loss of muscle mass and function, leading to disability, morbidity and increased mortality in older people. Given the relatively high prevalence and related- outcome of the disease, correct diagnosis, screening, monitoring and treatment of sarcopenia are needed in clinical practice. Recent researches have focused on cell-free nucleic acids, which are released into the circulation following cell death, as a potential biomarker of aging and systematic inflammation. It seems that the diagnosis and treatment of sarcopenia can be possible by the help of the analysis of cell-free nucleic acids as noninvasive method.

Versatile, sensitive liquid chromatography mass spectrometry - Implementation of 10 μm OT columns suitable for small molecules, peptides and proteins

We have designed a versatile and sensitive liquid chromatographic (LC) system, featuring a monolithic trap column and a very narrow (10 μm ID) fused silica open tubular liquid chromatography (OTLC) separation column functionalized with C18-groups, for separating a wide range of molecules (from small metabolites to intact proteins). Compared to today's capillary/nanoLC approaches, our system provides significantly enhanced sensitivity (up to several orders) with matching or improved separation efficiency, and highly repeatable chromatographic performance. The chemical properties of the trap column and the analytical column were fine-tuned to obtain practical sample loading capacities (above 2 μg), an earlier bottleneck of OTLC. Using the OTLC system (combined with Orbitrap mass spectrometry), we could perform targeted metabolomics of sub-μg amounts of exosomes with 25 attogram detection limit of a breast cancer-related hydroxylated cholesterol. With the same set-up, sensitive bottom-up proteomics (targeted and untargeted) was possible, and high-resolving intact protein analysis. In contrast to state-of-the-art packed columns, our platform performs chromatography with very little dilution and is "fit-for-all", well suited for comprehensive analysis of limited samples, and has potential as a tool for challenges in diagnostics.