Towards precision medicine

Assessing the role of interactivity: An evaluation of information aids to support the enrollment of precision medicine research programs

OBJECTIVE

Recruiting diverse participants for precision medicine (PM) research programs should overcome low literacy and varied expectations. Information aids (IA) can address these barriers through patient-centered education. The purpose of this study was to evaluate the effectiveness of three information aids (IA) on participating in PM.

METHODS

An experiment with 290 U.S. participants recruited from Mturk was conducted to compare the effects of three IAs on the outcomes related to participation. Three conditions included an interactive IA (i.e., providing PM-related information responding to each participant's questions), a static IA (i.e., providing uniform PM-related information), and a control condition (i.e., providing non-interactive information irrelevant to PM).

RESULTS

Both interactive and non-interactive IAs increased attitudes and information-seeking intentions, but not knowledge or participation intention. Perceived control and responsiveness mediated the effects of interactive IA.

CONCLUSION

Both interactive and static IAs supported enrollment efforts for PM through fostering attitudes and follow-up information-seeking. Increased perceived control and responsiveness are key to the effects of interactive IA.

PRACTICE IMPLICATIONS

IAs provide effective education and enrollment support for PM. Interactive IA can respond to individuals' inquiries and control the learning process.

Engineered Vasculature for Cancer Research and Regenerative Medicine

Engineered human tissues created by three-dimensional cell culture of human cells in a hydrogel are becoming emerging model systems for cancer drug discovery and regenerative medicine. Complex functional engineered tissues can also assist in the regeneration, repair, or replacement of human tissues. However, one of the main hurdles for tissue engineering, three-dimensional cell culture, and regenerative medicine is the capability of delivering nutrients and oxygen to cells through the vasculatures. Several studies have investigated different strategies to create a functional vascular system in engineered tissues and organ-on-a-chips. Engineered vasculatures have been used for the studies of angiogenesis, vasculogenesis, as well as drug and cell transports across the endothelium. Moreover, vascular engineering allows the creation of large functional vascular conduits for regenerative medicine purposes. However, there are still many challenges in the creation of vascularized tissue constructs and their biological applications. This review will summarize the latest efforts to create vasculatures and vascularized tissues for cancer research and regenerative medicine.

Shared Decision-Making Experiences of Couples with Inherited Cancer Risk Regarding Family Building

Patients with hereditary cancer predisposition syndromes have a high likelihood of passing germline mutations to future offspring. Patients at risk for inherited cancer may not have started and/or completed building their families; thus, they must decide about having children and consider the possibility of passing on their germline mutation. Utilizing the Shared Decision Making (SDM) Model, this study explores family building decision-making communication processes in opposite-sex couples with inherited cancer risk (ICR). Fifteen couples completed two recorded, analogue discussions and dyadic interviews at two time points. Participants were recruited through social media and snowball sampling. The constant comparison method was utilized to thematically analyze the data. When couples discussed family building options (FBOs), several themes were identified: FBO risks, FBO considerations, genetic-related FBO logistics, and life FBOs logistics. When deliberating family building decisions, couples shared easy conversational topics (e.g. FBO options and potential child's cancer risk due to a genetic variant) and difficult/conflict-inducing topics (e.g. preparing for possibilities, parenting, emotions, finances, and timing). Last, couples self-reported primary and secondary FBOs. The findings of this study capture couples' decision-making communication process while considering their experiences. Clinicians and practitioners can utilize these findings to support couples' family building decisions considering their ICR.

Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework

With the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making process of humans, using insights obtained from group-level interpretation of ML models for designing individual interventions may lead to mixed results. The present study proposes a hybrid ML framework by integrating established predictive and explainable ML approaches for decision support systems involving the prediction of human decisions and designing individualized interventions accordingly. The proposed framework is aimed at providing actionable insights for designing individualized interventions. It was showcased in the context of college students' attrition problem using a large and feature-rich integrated data set of freshman college students containing information about their demographics, educational, financial, and socioeconomic factors. A comparison of feature importance scores at the group- vs. individual-level revealed that while group-level insights might be useful for adjusting long-term strategies, using them as a one-size-fits-all strategy to design and implement individual interventions is subject to suboptimal outcomes.

Estrogenic flavonoids and their molecular mechanisms of action

Flavonoids are a major group of phytoestrogens associated with physiological effects, and ecological and social impacts. Although the estrogenic activity of flavonoids was reported by researchers in the fields of medical, environmental and food studies, their molecular mechanisms of action have not been comprehensively reviewed. The estrogenic activity of the respective classes of flavonoids, anthocyanidins/anthocyanins, 2-arylbenzofurans/3-arylcoumarins/α-methyldeoxybenzoins, aurones/chalcones/dihydrochalcones, coumaronochromones, coumestans, flavans/flavan-3-ols/flavan-4-ols, flavanones/dihydroflavonols, flavones/flavonols, homoisoflavonoids, isoflavans, isoflavanones, isoflavenes, isoflavones, neoflavonoids, oligoflavonoids, pterocarpans/pterocarpenes, and rotenone/rotenoids, was summarized through a comprehensive literature search, and their structure-activity relationship, biological activities, signaling pathways, and applications were discussed. Although the respective classes of flavonoids contained at least one chemical mimicking estrogen, the mechanisms varied, such as those with estrogenic, anti-estrogenic, non-estrogenic, and biphasic activities, and additional activities through crosstalk/bypassing, which exert biological activities through cell signaling pathways. Such mechanistic variations of estrogen action are not limited to flavonoids and are observed among other broad categories of chemicals, thus this group of chemicals can be termed as the "estrogenome". This review article focuses on the connection of estrogen action mainly between the outer and the inner environments, which represent variations of chemicals and biological activities/signaling pathways, respectively, and form the basis to understand their applications. The applications of chemicals will markedly progress due to emerging technologies, such as artificial intelligence for precision medicine, which is also true of the study of the estrogenome including estrogenic flavonoids.

Analytical device miniaturization for the detection of circulating biomarkers

Diverse (sub)cellular materials are secreted by cells into the systemic circulation at different stages of disease progression. These circulating biomarkers include whole cells, such as circulating tumour cells, subcellular extracellular vesicles and cell-free factors such as DNA, RNA and proteins. The biophysical and biomolecular state of circulating biomarkers carry a rich repertoire of molecular information that can be captured in the form of liquid biopsies for disease detection and monitoring. In this Review, we discuss miniaturized platforms that allow the minimally invasive and rapid detection and analysis of circulating biomarkers, accounting for their differences in size, concentration and molecular composition. We examine differently scaled materials and devices that can enrich, measure and analyse specific circulating biomarkers, outlining their distinct detection challenges. Finally, we highlight emerging opportunities in biomarker and device integration and provide key future milestones for their clinical translation.

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.

DROEG: a method for cancer drug response prediction based on omics and essential genes integration

Predicting therapeutic responses in cancer patients is a major challenge in the field of precision medicine due to high inter- and intra-tumor heterogeneity. Most drug response models need to be improved in terms of accuracy, and there is limited research to assess therapeutic responses of particular tumor types. Here, we developed a novel method DROEG (Drug Response based on Omics and Essential Genes) for prediction of drug response in tumor cell lines by integrating genomic, transcriptomic and methylomic data along with CRISPR essential genes, and revealed that the incorporation of tumor proliferation essential genes can improve drug sensitivity prediction. Concisely, DROEG integrates literature-based and statistics-based methods to select features and uses Support Vector Regression for model construction. We demonstrate that DROEG outperforms most state-of-the-art algorithms by both qualitative (prediction accuracy for drug-sensitive/resistant) and quantitative (Pearson correlation coefficient between the predicted and actual IC50) evaluation in Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia datasets. In addition, DROEG is further applied to the pan-gastrointestinal tumor with high prevalence and mortality as a case study at both cell line and clinical levels to evaluate the model efficacy and discover potential prognostic biomarkers in Cisplatin and Epirubicin treatment. Interestingly, the CRISPR essential gene information is found to be the most important contributor to enhance the accuracy of the DROEG model. To our knowledge, this is the first study to integrate essential genes with multi-omics data to improve cancer drug response prediction and provide insights into personalized precision treatment.

A review on longitudinal data analysis with random forest

In longitudinal studies variables are measured repeatedly over time, leading to clustered and correlated observations. If the goal of the study is to develop prediction models, machine learning approaches such as the powerful random forest (RF) are often promising alternatives to standard statistical methods, especially in the context of high-dimensional data. In this paper, we review extensions of the standard RF method for the purpose of longitudinal data analysis. Extension methods are categorized according to the data structures for which they are designed. We consider both univariate and multivariate response longitudinal data and further categorize the repeated measurements according to whether the time effect is relevant. Even though most extensions are proposed for low-dimensional data, some can be applied to high-dimensional data. Information of available software implementations of the reviewed extensions is also given. We conclude with discussions on the limitations of our review and some future research directions.

In-Tumor Biosynthetic Construction of Upconversion Nanomachines for Precise Near-Infrared Phototherapy

Targeted construction of therapeutic nanoplatforms in tumor cells with specific activation remains appealing but challenging. Here, we design a cancer-motivated upconversion nanomachine (UCNM) based on porous upconversion nanoparticles (p-UCNPs) for precise phototherapy. The nanosystem is equipped with a telomerase substrate (TS) primer and simultaneously encapsulates 5-aminolevulinic acid (5-ALA) and d-arginine (d-Arg). After coating with hyaluronic acid (HA), it can readily get into tumor cells, where 5-ALA induces efficient accumulation of protoporphyrin IX (PpIX) via the inherent biosynthetic pathway, and the overexpressed telomerase prolonged the TS to form G-quadruplexes (G4) for binding the resulting PpIX as a nanomachine. This nanomachine can respond to near-infrared (NIR) light and promote the active singlet oxygen (¹O₂) production due to the efficiency of Förster resonance energy transfer (FRET) between p-UCNPs and PpIX. Intriguingly, such oxidative stress can oxidize d-Arg into nitric oxide (NO), which relieves the tumor hypoxia and in turn improves the phototherapy effect. This in situ assembly approach significantly enhances targeting in cancer therapy and might be of considerable clinical value.

Aptamers and nanobodies as alternatives to antibodies for ligand-targeted drug delivery in cancer

Targeted drug delivery (TDD) is the selective delivery of a therapeutic agent specifically to the site of action to avoid adverse effects and systemic toxicity and to reduce the dose required. Ligand TDD or active TDD involves using a ligand-drug conjugate comprising a targeting ligand linked to an active drug moiety that can either be free or encapsulated within a nanocarrier (NC). Aptamers are single-stranded oligonucleotides that bind to specific biomacromolecules because of their 3D conformation. Nanobodies are the variable domains of unique heavy chain-only antibodies (HcAbs) produced by animals of the Camelidae family. Both these types of ligand are smaller than antibodies and have been used to efficiently target drugs to particular tissues or cells. In this review, we describe the applications of aptamers and nanobodies as ligands for TDD, their advantages and disadvantages compared with antibodies, and the various modalities for targeting cancers using these ligands. Teaser: Aptamers and nanobodies are macromolecular ligands that can actively chaperone drug molecules to particular cancerous cells or tissues in the body to target their pharmacological effects and improve their therapeutic index and safety.

Detecting impaired muscle relaxation in myopathies with the use of motor cortical stimulation

Impaired muscle relaxation is a notable feature in specific myopathies. Transcranial magnetic stimulation (TMS) of the motor cortex can induce muscle relaxation by abruptly halting corticospinal drive. Our aim was to quantify muscle relaxation using TMS in different myopathies with symptoms of muscle stiffness, contractures/cramps, and myalgia and explore the technique's diagnostic potential. In men, normalized peak relaxation rate was lower in Brody disease (n = 4) (-3.5 ± 1.3 s^-1), nemaline myopathy type 6 (NEM6; n = 5) (-7.5 ± 1.0 s^-1), and myotonic dystrophy type 2 (DM2; n = 5) (-10.2 ± 2.0 s^-1) compared to healthy (n = 14) (-13.7 ± 2.1 s^-1; all P ≤ 0.01) and symptomatic controls (n = 9) (-13.7 ± 1.6 s^-1; all P ≤ 0.02). In women, NEM6 (n = 5) (-5.7 ± 2.1 s^-1) and McArdle patients (n = 4) (-6.6 ± 1.4 s^-1) had lower relaxation rate compared to healthy (n = 10) (-11.7 ± 1.6 s^-1; both P ≤ 0.002) and symptomatic controls (n = 8) (-11.3 ± 1.8 s^-1; both P ≤ 0.008). TMS-induced muscle relaxation achieved a high level of diagnostic accuracy (area under the curve = 0.94 (M) and 0.92 (F)) to differentiate symptomatic controls from myopathy patients. Muscle relaxation assessed using TMS has the potential to serve as a diagnostic tool, an in-vivo functional test to confirm the pathogenicity of unknown variants, an outcome measure in clinical trials, and monitor disease progression.

Evaluation of Breast Implant-Associated Anaplastic Large Cell Lymphoma With Whole Exome and Genome Sequencing

BACKGROUND

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare malignancy originating from the periprosthetic capsule of a textured, most often macrotextured, breast implant. Identified in women whose indications for breast implants can be either aesthetic or reconstructive, the genomic underpinnings of this disease are only beginning to be elucidated.

OBJECTIVES

The aim of this study was to evaluate the exomes, and in some cases the entire genome, of patients with BIA-ALCL. Specific attention was paid to copy number alterations, chromosomal translocations, and other genomic abnormalities overrepresented in patients with BIA-ALCL.

METHODS

Whole-exome sequencing was performed on 6 patients, and whole-genome sequencing on 3 patients, with the Illumina NovaSeq 6000 sequencer. Data were analyzed with the Illumina DRAGEN Bio-IT Platform and the ChromoSeq pipeline. The Pathseq Genome Analysis Toolkit pipeline was used to detect the presence of microbial genomes in the sequenced samples.

RESULTS

Two cases with STAT3 mutations and 2 cases with NRAS mutations were noted. A critically deleted 7-Mb region was identified at the 11q22.3 region of chromosome 11, and multiple nonrecurrent chromosomal rearrangements were identified by whole-genome sequencing. Recurrent gene-level rearrangements, however, were not identified. None of the samples showed evidence of potential microbial pathogens.

CONCLUSIONS

Although no recurrent mutations were identified, this study identified mutations in genes not previously reported with BIA-ALCL or other forms of ALCL. Furthermore, not previously reported with BIA-ALCL, 11q22.3 deletions were consistent across whole-genome sequencing cases and present in some exomes.

LEVEL OF EVIDENCE: 5

DNA-Methylation Signatures of Tobacco Smoking in a High Cardiovascular Risk Population: Modulation by the Mediterranean Diet

Biomarkers based on DNA methylation are relevant in the field of environmental health for precision health. Although tobacco smoking is one of the factors with a strong and consistent impact on DNA methylation, there are very few studies analyzing its methylation signature in southern European populations and none examining its modulation by the Mediterranean diet at the epigenome-wide level. We examined blood methylation smoking signatures on the EPIC 850 K array in this population (n = 414 high cardiovascular risk subjects). Epigenome-wide methylation studies (EWASs) were performed analyzing differential methylation CpG sites by smoking status (never, former, and current smokers) and the modulation by adherence to a Mediterranean diet score was explored. Gene-set enrichment analysis was performed for biological and functional interpretation. The predictive value of the top differentially methylated CpGs was analyzed using receiver operative curves. We characterized the DNA methylation signature of smoking in this Mediterranean population by identifying 46 differentially methylated CpGs at the EWAS level in the whole population. The strongest association was observed at the cg21566642 (p = 2.2 × 10-32) in the 2q37.1 region. We also detected other CpGs that have been consistently reported in prior research and discovered some novel differentially methylated CpG sites in subgroup analyses. In addition, we found distinct methylation profiles based on the adherence to the Mediterranean diet. Particularly, we obtained a significant interaction between smoking and diet modulating the cg5575921 methylation in the AHRR gene. In conclusion, we have characterized biomarkers of the methylation signature of tobacco smoking in this population, and suggest that the Mediterranean diet can increase methylation of certain hypomethylated sites.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

Chemically engineering cells for precision medicine

Cell-based therapy holds great potential to address unmet medical needs and revolutionize the healthcare industry, as demonstrated by several therapeutics such as CAR-T cell therapy and stem cell transplantation that have achieved great success clinically. Nevertheless, natural cells are often restricted by their unsatisfactory in vivo trafficking and lack of therapeutic payloads. Chemical engineering offers a cost-effective, easy-to-implement engineering tool that allows for strengthening the inherent favorable features of cells and confers them new functionalities. Moreover, in accordance with the trend of precision medicine, leveraging chemical engineering tools to tailor cells to accommodate patients individual needs has become important for the development of cell-based treatment modalities. This review presents a comprehensive summary of the currently available chemically engineered tools, introduces their application in advanced diagnosis and precision therapy, and discusses the current challenges and future opportunities.

Microneedles as a momentous platform for psoriasis therapy and diagnosis: A state-of-the-art review

Psoriasis is a chronic, autoimmune, and non-communicable skin disease with a worldwide prevalence rate of 2-3%, creating an economic burden on global health. Some significant risk factors associated with psoriasis include genetic predisposition, pathogens, stress, medications, etc. In addition, most patients with psoriasis should also deal with comorbidities such as psoriatic arthritis, inflammatory bowel diseases, cardiovascular diseases, and psychological conditions, including suicidal thoughts. Based on its severity, the treatment approach for psoriasis is categorised into three types, i.e., topical therapy, systemic therapy, and phototherapy. Topical therapy for mild-to-moderate psoriasis faces several issues, such as poor skin permeability, low skin retention of drug formulation, greasy texture of topical vehicle, lack of controlled release, and so on. On the other arrow, systemic therapy via an oral or parenteral route of drug administration involves numerous drawbacks, including first-pass hepatic metabolism, hepatotoxicity, gastrointestinal disturbances, needle pain and phobia, and requirement of healthcare professional to administer the drug. To overcome these limitations, researchers devised a microneedle-based drug delivery system for treating mild-to-moderate and moderate-to-severe psoriasis. A single microneedle system can deliver the anti-psoriatic drugs either locally (topical) or systemically (transdermal) by adjusting the needle height without involving any pain. In this contemplate, the current review provides concise information on the pathophysiology, risk factors, and comorbidities of psoriasis, followed by their current treatment approaches and limitations. Further, it meticulously discusses the potential of microneedles in psoriasis therapy and diagnosis, along with descriptions of their patents and clinical trials.

Past Myocardial Infarctions and Gender Predict the LVEF Regardless of the Status of Coronary Collaterals: An AI-Informed Research

BACKGROUND: The degree of the development of coronary collaterals is long considered an alternate – that is, a collateral – source of blood supply to an area of the myocardium threatened with vascular ischemia or insufficiency. Hence, the coronary collaterals are beneficial but can also promote harmful (adverse) effects. For instance, the coronary steal effect during the myocardial hyperemia phase and that of restenosis following coronary angioplasty. OBJECTIVES: Our study explores the contribution of coronary collaterals – if any exist – while considering other potential predictors, including demographics and medical history, toward the left ventricular (LV) dysfunction measured through the LV ejection fraction (LVEF). METHODS: Our cross-sectional design study used convenience sampling of 100 patients (n = 100; a male-to-female ratio of 4:1). We conducted frequentist inference statistics using IBM-SPSS version 24 and Microsoft Office Excel 2016 with the analysis ToolPak plugin; we ran parallel neural networks (supervised machine learning (ML)) and a two-step clustering (non-supervised ML) for robust conjoint inference with frequentist statistics. RESULTS: The past incidents of myocardial infarction (p = 0.036) and gender (p = 0.072) influenced the LVEF; both are significant predictors at a 90% confidence interval. We found that gender and past incidents of MI influenced the LVEF regardless of the status of coronary collaterals. Our study did not yield any positive or significant findings concerning the status of coronary collaterals or the coronary circulation dominance patterns. CONCLUSION: Regardless of the status of coronary collaterals, we verified that the female gender is protective of the LV function, contrary to the past infarction incidents that predispose to a deteriorated LV function. Our study’s innovation relates to its status as the first study from India to explore the coronary collaterals and the ejection fraction while incorporating frequentist statistics and narrow artificial intelligence to infer reliable results.

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.

Investigations into the use of machine learning to predict drug dosage form design to obtain desired release profiles for 3D printed oral medicines

Three-dimensional (3D) printing, digitalization, and artificial intelligence (AI) are gaining increasing interest in modern medicine. All three aspects are combined in personalized medicine where 3D-printed dosage forms are advantageous because of their variable geometry design. The geometry design can be used to determine the surface area to volume (SA/V) ratio, which affects drug release from the dosage forms. This study investigated artificial neural networks (ANN) to predict suitable geometries for the desired dose and release profile. Filaments with 5% API load and polyvinyl alcohol were 3D printed using Fused Deposition Modeling to provide a wide variety of geometries with different dosages and SA/V ratios. These were dissolved in vitro, and the API release profiles were described mathematically. Using these data, ANN architectures were designed with the goal of predicting a suitable dosage form geometry. Poor accuracies of 68.5% in the training and 44.4% in the test settings were achieved with a classification architecture. However, the SA/V ratio could be predicted accurately with a mean squared error loss of only 0.05. This study shows that the prediction of the SA/V ratio using AI works, but not of the exact geometry. For this purpose, a global database could be built with a range of geometries to simplify the prescription process.

PAnno: A pharmacogenomics annotation tool for clinical genomic testing

Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno. Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants.

Degrader-Antibody Conjugates: Emerging New Modality

In order to deliver chemotherapeutics more efficiently, small-molecule-drug conjugates (SMDCs) and antibody-drug conjugates (ADCs) have been synthesized and explored. These conjugates not only provide selective delivery but also improve the therapeutic index of toxins. By merging this conjugate concept with target protein degradation (TPD), the degrader-antibody conjugate (DAC) field has emerged, and clinical trials have even begun in recent years. In this Perspective, we provide the concepts, applications, and recent advances in the area of DACs.

Public perceptions and attitudes of the national project of bio-big data: A nationwide survey in the Republic of Korea

Background: The National Project of Bio-Big Data (NPBBD) is a South Korean bio-big data collection project, expected to include health, genomic, and lifelog data of one million Koreans. The Ethical, Legal, and Social Implications study is a parallel study active since 2020. As part of the study, a public survey was conducted to evaluate public attitudes towards engagement schemes, such as public committees and web portals for communication between the public and researchers. Methods: An online survey was conducted from March 3-9, 2021, using structured questionnaires addressed to 1,000 adults aged 20-59 years. Results: Several respondents reported a positive attitude towards participation (43.6% "somewhat," 14.3% "definitely"), whereas approximately one-third (36.5%) reported a neutral attitude. Positive factors that may affect the willingness of the respondents to participate included receiving health information (25.1%), contributing to research on cancer and rare diseases (21.9%), and advancing personalized medicine (21.5%). Conversely, negative factors were mainly associated with concerns regarding the risk of data leakage (22.8%), discrimination (21.1%), lack of information (13.5%), possibility of knowing the risk of being diagnosed with an incurable diseases (12.5%), and possibility of using data in industry (11.3%). In terms of project governance, respondents tended to recognize the importance of public participation in incorporating public opinion into the project design. Conclusion: These results have implications for the participant recruitment process, public engagement strategies, and the scope of user (academics/industry, domestic/overseas) accessibility to the database.

Genomic medicine in Africa: a need for molecular genetics and pharmacogenomics experts

The large-scale implementation of genomic medicine in Africa has not been actualized. This overview describes how routine molecular genetics and advanced protein engineering/structural biotechnology could accelerate the implementation of genomic medicine. By using data-mining and analysis approaches, we analyzed relevant information obtained from public genomic databases on pharmacogenomics biomarkers and reviewed published studies to discuss the ideas. The results showed that only 68 very important pharmacogenes currently exist, while 867 drug label annotations, 201 curated functional pathways, and 746 annotated drugs have been catalogued on the largest pharmacogenomics database (PharmGKB). Only about 5009 variants of the reported ∼25,000 have been clinically annotated. Predominantly, the genetic variants were derived from 43 genes that contribute to 2318 clinically relevant variations in 57 diseases. Majority (∼60%) of the clinically relevant genetic variations in the pharmacogenes are missense variants (1390). The enrichment analysis showed that 15 pharmacogenes are connected biologically and are involved in the metabolism of cardiovascular and cancer drugs. The review of studies showed that cardiovascular diseases are the most frequent non-communicable diseases responsible for approximately 13% of all deaths in Africa. Also, warfarin pharmacogenomics is the most studied drug on the continent, while CYP2D6, CYP2C9, DPD, and TPMT are the most investigated pharmacogenes with allele activities indicated in African and considered to be intermediate metaboliser for DPD and TPMT (8.4% and 11%). In summary, we highlighted a framework for implementing genomic medicine starting from the available resources on ground.

Quantitative differentiation of benign and misfolded glaucoma-causing myocilin variants on the basis of protein thermal stability

Accurate predictions of the pathogenicity of mutations associated with genetic diseases are key to the success of precision medicine. Inherited missense mutations in the myocilin (MYOC) gene, within its olfactomedin (OLF) domain, constitute the strongest genetic link to primary open-angle glaucoma via a toxic gain of function, and thus MYOC is an attractive precision-medicine target. However, not all mutations in MYOC cause glaucoma, and common variants are expected to be neutral polymorphisms. The Genome Aggregation Database (gnomAD) lists ∼100 missense variants documented within OLF, all of which are relatively rare (allele frequency <0.001%) and nearly all are of unknown pathogenicity. To distinguish disease-causing OLF variants from benign OLF variants, we first characterized the most prevalent population-based variants using a suite of cellular and biophysical assays, and identified two variants with features of aggregation-prone familial disease variants. Next, we considered all available biochemical and clinical data to demonstrate that pathogenic and benign variants can be differentiated statistically based on a single metric: the thermal stability of OLF. Our results motivate genotyping MYOC in patients for clinical monitoring of this widespread, painless and irreversible ocular disease.

An Untargeted Metabolomics Workflow that Scales to Thousands of Samples for Population-Based Studies

The success of precision medicine relies upon collecting data from many individuals at the population level. Although advancing technologies have made such large-scale studies increasingly feasible in some disciplines such as genomics, the standard workflows currently implemented in untargeted metabolomics were developed for small sample numbers and are limited by the processing of liquid chromatography/mass spectrometry data. Here we present an untargeted metabolomics workflow that is designed to support large-scale projects with thousands of biospecimens. Our strategy is to first evaluate a reference sample created by pooling aliquots of biospecimens from the cohort. The reference sample captures the chemical complexity of the biological matrix in a small number of analytical runs, which can subsequently be processed with conventional software such as XCMS. Although this generates thousands of so-called features, most do not correspond to unique compounds from the samples and can be filtered with established informatics tools. The features remaining represent a comprehensive set of biologically relevant reference chemicals that can then be extracted from the entire cohort's raw data on the basis of m/z values and retention times by using Skyline. To demonstrate applicability to large cohorts, we evaluated >2000 human plasma samples with our workflow. We focused our analysis on 360 identified compounds, but we also profiled >3000 unknowns from the plasma samples. As part of our workflow, we tested 14 different computational approaches for batch correction and found that a random forest-based approach outperformed the others. The corrected data revealed distinct profiles that were associated with the geographic location of participants.

Strategy of Patient-Specific Therapeutics in Cardiovascular Disease Through Single-Cell RNA Sequencing

Recently, single cell RNA sequencing (scRNA-seq) technology has enabled the discovery of novel or rare subtypes of cells and their characteristics. This technique has advanced unprecedented biomedical research by enabling the profiling and analysis of the transcriptomes of single cells at high resolution and throughput. Thus, scRNA-seq has contributed to recent advances in cardiovascular research by the generation of cell atlases of heart and blood vessels and the elucidation of mechanisms involved in cardiovascular development and diseases. This review summarizes the overall workflow of the scRNA-seq technique itself and key findings in the cardiovascular development and diseases based on the previous studies. In particular, we focused on how the single-cell sequencing technology can be utilized in clinical field and precision medicine to treat specific diseases.

Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived Tumor-Cell Spheroids

Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug-induced cell-death phenotypes. Here, we aim to quantify image-based drug responses in patient-derived 3D spheroid tumor cell cultures, tackling the challenges of a lack of single-cell-segmentation methods and limited patient-derived material. Therefore, we investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with 210 control images specific to a single training cell line and 54 additional screen -specific assay control images. This method of image-based drug profiling is validated on 6 cell lines with known drug sensitivities, and further tested with primary patient-derived samples in a medium-throughput setting. Network outputs at different drug concentrations are used for drug-sensitivity scoring, and dense-layer activations are used in t-distributed stochastic neighbor embeddings of drugs to visualize groups of drugs with similar cell-death phenotypes. Image-based cell-line experiments show strong correlation to metabolic results ( R ≈ 0.7 ) and confirm expected hits, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derived samples, combining drug sensitivity scoring with phenotypic analysis may provide opportunities for complementary combination treatments. Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery.

Phenomics in sport: Can emerging methodology drive advanced insights?

Methodologies in applied sport science have predominantly driven a reductionist grounding to component-specific mechanisms to drive athlete training and care. While linear mechanistic approaches provide useful insights, they have impeded progress in the development of more complex network physiology models that consider the temporal and spatial interactions of multiple factors within and across systems and subsystems. For this, a more sophisticated approach is needed and the development of such a methodological framework can be considered a Sport Grand Challenge. Specifically, a transdisciplinary phenomics-based scientific and modeling framework has merit. Phenomics is a relatively new area in human precision medicine, but it is also a developed area of research in the plant and evolutionary biology sciences. The convergence of innovative precision medicine, portable non-destructive measurement technologies, and advancements in modeling complex human behavior are central for the integration of phenomics into sport science. The approach enables application of concepts such as phenotypic fitness, plasticity, dose-response dynamics, critical windows, and multi-dimensional network models of behavior. In addition, profiles are grounded in indices of change, and models consider the athlete's performance or recovery trajectory as a function of their dynamic environment. This new framework is introduced across several example sport science domains for potential integration. Specific factors of emphasis are provided as potential candidate fitness variables and example profiles provide a generalizable modeling approach for precision training and care. Finally, considerations for the future are discussed, including scaling from individual athletes to teams and additional factors necessary for the successful implementation of phenomics.

Inappropriate Evaluation of Effect Modifications Based on Categorical Outcomes: A Systematic Review of Randomized Controlled Trials

Our meta-epidemiological study aimed to describe the prevalence of reporting effect modification only on relative scale outcomes and inappropriate interpretations of the coefficient of interaction terms in nonlinear models on categorical outcomes. Our study targeted articles published in the top 10 high-impact-factor journals between 1 January and 31 December 2021. We included two-arm, parallel-group, interventional superiority randomized controlled trials to evaluate the effects of modifications on categorical outcomes. The primary outcomes were the prevalence of reporting effect modifications only on relative scale outcomes and that of inappropriately interpreting the coefficient of interaction terms in nonlinear models on categorical outcomes. We included 52 articles, of which 41 (79%) used nonlinear regression to evaluate effect modifications. At least 45/52 articles (87%) reported effect modifications based only on relative scale outcomes, and at least 39/41 (95%) articles inappropriately interpreted the coefficient of interaction terms merely as indices of effect modifications. The quality of the evaluations of effect modifications in nonlinear models on categorical outcomes was relatively low, even in randomized controlled trials published in medical journals with high impact factors. Researchers should report effect modifications of both absolute and relative scale outcomes and avoid interpreting the coefficient of interaction terms in nonlinear regression analyses.

Machine learning based personalized drug response prediction for lung cancer patients

Lung cancers with a mutated epidermal growth factor receptor (EGFR) are a major contributor to cancer fatalities globally. Targeted tyrosine kinase inhibitors (TKIs) have been developed against EGFR and show encouraging results for survival rate and quality of life. However, drug resistance may affect treatment plans and treatment efficacy may be lost after about a year. Predicting the response to EGFR-TKIs for EGFR-mutated lung cancer patients is a key research area. In this study, we propose a personalized drug response prediction model (PDRP), based on molecular dynamics simulations and machine learning, to predict the response of first generation FDA-approved small molecule EGFR-TKIs, Gefitinib/Erlotinib, in lung cancer patients. The patient's mutation status is taken into consideration in molecular dynamics (MD) simulation. Each patient's unique mutation status was modeled considering MD simulation to extract molecular-level geometric features. Moreover, additional clinical features were incorporated into machine learning model for drug response prediction. The complete feature set includes demographic and clinical information (DCI), geometrical properties of the drug-target binding site, and the binding free energy of the drug-target complex from the MD simulation. PDRP incorporates an XGBoost classifier, which achieves state-of-the-art performance with 97.5% accuracy, 93% recall, 96.5% precision, and 94% F1-score, for a 4-class drug response prediction task. We found that modeling the geometry of the binding pocket combined with binding free energy is a good predictor for drug response. However, we observed that clinical information had a little impact on the performance of the model. The proposed model could be tested on other types of cancers. We believe PDRP will support the planning of effective treatment regimes based on clinical-genomic information. The source code and related files are available on GitHub at:   https://github.com/rizwanqureshi123/PDRP/ .

Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information

Since U.S. President Barack Obama announced the Precision Medicine Initiative in his New Year's State of the Union address in 2015, the establishment of a precision medicine system has been emphasized worldwide, particularly in the field of oncology. With the advent of next-generation sequencers specifically, genome analysis technology has made remarkable progress, and there are active efforts to apply genome information to diagnosis and treatment. Generally, in the process of feeding back the results of next-generation sequencing analysis to patients, a molecular tumor board (MTB), consisting of experts in clinical oncology, genetic medicine, etc., is established to discuss the results. On the other hand, an MTB currently involves a large amount of work, with humans searching through vast databases and literature, selecting the best drug candidates, and manually confirming the status of available clinical trials. In addition, as personalized medicine advances, the burden on MTB members is expected to increase in the future. Under these circumstances, introducing cutting-edge artificial intelligence (AI) technology and information and communication technology to MTBs while reducing the burden on MTB members and building a platform that enables more accurate and personalized medical care would be of great benefit to patients. In this review, we introduced the latest status of elemental technologies that have potential for AI utilization in MTB, and discussed issues that may arise in the future as we progress with AI implementation.

Next-Generation Examination, Diagnosis, and Personalized Medicine in Periodontal Disease

Periodontal disease, a major cause of tooth loss, is an infectious disease caused by bacteria with the additional aspect of being a noncommunicable disease closely related to lifestyle. Tissue destruction based on chronic inflammation is influenced by host and environmental factors. The treatment of periodontal disease varies according to the condition of each individual patient. Although guidelines provide standardized treatment, optimization is difficult because of the wide range of treatment options and variations in the ideas and skills of the treating practitioner. The new medical concepts of “precision medicine” and “personalized medicine” can provide more predictive treatment than conventional methods by stratifying patients in detail and prescribing treatment methods accordingly. This requires a new diagnostic system that integrates information on individual patient backgrounds (biomarkers, genetics, environment, and lifestyle) with conventional medical examination information. Currently, various biomarkers and other new examination indices are being investigated, and studies on periodontal disease-related genes and the complexity of oral bacteria are underway. This review discusses the possibilities and future challenges of precision periodontics and describes the new generation of laboratory methods and advanced periodontal disease treatment approaches as the basis for this new field.

Systems Medicine Approach for Tinnitus with Comorbid Disorders

Despite the fact that chronic diseases usually occur together with a spectrum of possible comorbidities that may differ strongly between patients, they are classically still viewed as distinct disease entities and, consequently, are often treated with uniform therapies. Unfortunately, such an approach does not take into account that different combinations of symptoms and comorbidities may result from different pathological (e.g., environmental, genetic, dietary, etc.) factors, which require specific and individualised therapeutic strategies. In this opinion paper, we aim to put forward a more differentiated, systems medicine approach to disease and patient treatment. To elaborate on this concept, we focus on the interplay of tinnitus, depression, and chronic pain. In our view, these conditions can be characterised by a variety of phenotypes composed of variable sets of symptoms and biomarkers, rather than distinct disease entities. The knowledge of the interplay of such symptoms and biomarkers will provide the key to a deeper, mechanistic understanding of disease pathologies. This paves the way for prediction and prevention of disease pathways, including more personalised and effective treatment strategies.

Anti-atherosclerotic therapies: Milestones, challenges, and emerging innovations

Atherosclerosis is the main underlying pathology for many cardiovascular diseases (CVDs), which are the leading cause of death globally and represent a serious health crisis. Atherosclerosis is a chronic condition that can lead to myocardial infarction, ischemic cardiomyopathy, stroke, and peripheral arterial disease. Elevated plasma lipids, hypertension, and high glucose are the major risk factors for developing atherosclerotic plaques. To date, most pharmacological therapies aim to control these risk factors, but they do not target the plaque-causing cells themselves. In patients with acute coronary syndromes, surgical revascularization with percutaneous coronary intervention has greatly reduced mortality rates. However, stent thrombosis and neo-atherosclerosis have emerged as major safety concerns of drug eluting stents due to delayed re-endothelialization. This review summarizes the major milestones, strengths, and limitations of current anti-atherosclerotic therapies. It provides an overview of the recent discoveries and emerging game-changing technologies in the fields of nanomedicine, mRNA therapeutics, and gene editing that have the potential to revolutionize CVD clinical practice by steering it toward precision medicine.

Ethical considerations for precision psychiatry: A roadmap for research and clinical practice

Precision psychiatry is an emerging field with transformative opportunities for mental health. However, the use of clinical prediction models carries unprecedented ethical challenges, which must be addressed before accessing the potential benefits of precision psychiatry. This critical review covers multidisciplinary areas, including psychiatry, ethics, statistics and machine-learning, healthcare and academia, as well as input from people with lived experience of mental disorders, their family, and carers. We aimed to identify core ethical considerations for precision psychiatry and mitigate concerns by designing a roadmap for research and clinical practice. We identified priorities: learning from somatic medicine; identifying precision psychiatry use cases; enhancing transparency and generalizability; fostering implementation; promoting mental health literacy; communicating risk estimates; data protection and privacy; and fostering the equitable distribution of mental health care. We hope this blueprint will advance research and practice and enable people with mental health problems to benefit from precision psychiatry.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

Transcriptomics and RNA-Based Therapeutics as Potential Approaches to Manage SARS-CoV-2 Infection

SARS-CoV-2 is a coronavirus family member that appeared in China in December 2019 and caused the disease called COVID-19, which was declared a pandemic in 2020 by the World Health Organization. In recent months, great efforts have been made in the field of basic and clinical research to understand the biology and infection processes of SARS-CoV-2. In particular, transcriptome analysis has contributed to generating new knowledge of the viral sequences and intracellular signaling pathways that regulate the infection and pathogenesis of SARS-CoV-2, generating new information about its biology. Furthermore, transcriptomics approaches including spatial transcriptomics, single-cell transcriptomics and direct RNA sequencing have been used for clinical applications in monitoring, detection, diagnosis, and treatment to generate new clinical predictive models for SARS-CoV-2. Consequently, RNA-based therapeutics and their relationship with SARS-CoV-2 have emerged as promising strategies to battle the SARS-CoV-2 pandemic with the assistance of novel approaches such as CRISPR-CAS, ASOs, and siRNA systems. Lastly, we discuss the importance of precision public health in the management of patients infected with SARS-CoV-2 and establish that the fusion of transcriptomics, RNA-based therapeutics, and precision public health will allow a linkage for developing health systems that facilitate the acquisition of relevant clinical strategies for rapid decision making to assist in the management and treatment of the SARS-CoV-2-infected population to combat this global public health problem.

Radiotherapy-triggered prodrug activation: A new era in precise chemotherapy

A major goal of cancer therapy is developing chemotherapy strategies that are accurate and efficient without being highly toxic. Recently, Bradley et al.¹ and Liu et al.² developed radioresponsive prodrugs, enabling site-directed, radiotherapy-triggered, and precise chemotherapy, which displays synergistic efficacy and avoids the serious side effects of conventional treatment approaches.

Mass Spectrometry-Based Chemical Proteomics for Drug Target Discoveries

Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.

Reducing Knee Joint Load during a Golf Swing: The Effects of Ball Position Modification at Address

As the modern golf swing has changed, the incidence of knee pain in professional golfers is increasing. For those with previous knee injuries, developing a golf-swing modification that reduces knee loading may be necessary to recover performance after injury. The purpose of this study was to test whether ball position modification reduces knee joint load in a golf swing. Thirteen male professional golfers participated in the study. Golf swings were captured using a three-dimensional motion capture system and two force platforms, with conditions for self-selected ball position and eight additional ball positions. Knee internal rotation and adduction moments were calculated. The length of one golf ball (4.27 cm) backward ball position (closer to the golfer) significantly reduced the peak internal rotation moment of the lead knee (- 13.8%) (p &lt; 0.001) and the length of one golf ball (4.27 cm) away from the target ball position significantly reduced the peak adduction moment of the lead knee (- 11.5%) (p &lt; 0.001) compared with that of the self-selected ball position. Based on these observations, we conclude that the backward ball position modification might be suggested for golfers with anterior cruciate ligament injuries, and the away from the target modification might be suggested for golfers with medial compartment knee osteoarthritis.

Discovery of mobocertinib, a new irreversible tyrosine kinase inhibitor indicated for the treatment of non-small-cell lung cancer harboring EGFR exon 20 insertion mutations

Epidermal growth factor receptor (EGFR) is essential for normal cellular functions. Mutations of EGFR's kinase domain can cause dysregulation leading to non-small cell lung cancer (NSCLC). Exon 20 insertion (ex20ins) mutations in EGFR are one of the leading contributors to oncogenesis and confer insensitivity to most available therapeutics. Mobocertinib is a novel tyrosine kinase inhibitor (TKI) recently approved by the US FDA as a first-in-class small molecule therapeutic for EGFR ex20ins-positive NSCLC. When compared to osimertinib, a TKI indicated for the treatment of EGFR T790M-positive NSCLC, mobocertinib differs only by the presence of an additional C5-carboxylate isopropyl ester group on the middle pyrimidine core. Together with the acrylamide side chain that is responsible for irreversible inhibition, this additional C5-substituent affords mobocertinib high anticancer potency and specificity to EGFR ex20ins-positive lung cancer that is resistant to other EGFR TKIs. This review article provides an overview of the discovery of mobocertinib from osimertinib including their structure-activity relationships, mechanisms of action, preclinical pharmacology, pharmacokinetics, and clinical applications. The discovery and use of mobocertinib and other EGFR TKIs demonstrate the power of structure-based drug design and promising therapeutic outcomes of using precision medicine approaches in the management of molecularly defined tumors. Graphical abstract.