Familial hypobetalipoproteinemia (FHBL), caused by apolipoprotein B (APOB) variants, disrupts APOB-containing lipoprotein synthesis, leading to reduced serum total cholesterol, low-density lipoprotein cholesterol, and APOB. Heterozygous carriers are often asymptomatic, while homozygotes exhibit severe manifestations like malabsorption, vitamin deficiencies, and hepatic steatosis. In recent years, FHBL has attracted increasing attention due to its association with liver disease and its role as a unique monogenic model of steatotic liver disease independent of cardiometabolic risk factors. Mechanistically, lipid overload, endoplasmic reticulum stress, oxidative damage, and impaired autophagy may drive hepatocellular injury and fibrosis. Challenges include insufficient diagnosis, sparse epidemiological data, and unclear disease progression. Enhanced genetic testing, mechanistic research, and longitudinal studies are critical to improving diagnosis, risk assessment, and therapies for FHBL-associated liver disease.

Despite rapid advances in computational biology and regulatory reforms encouraging the reduction of animal use, a clear synthesis of how artificial intelligence (AI)-driven polypharmacology can function as a scientific and ethical bridge between traditional in vivo pharmacology and human-relevant drug development remains lacking. The shift from cage-based experimentation to code-based predictive modeling presents both opportunities and unresolved challenges in biological interpretation, regulatory acceptance, and pharmacology education. Therefore, this review aims to critically examine the transition toward AI-enabled, human-centric drug discovery within the framework of the 3R principles (Replacement, Reduction, and Refinement). Specifically, it explores (i) the global regulatory and ethical drivers accelerating non-animal methodologies, (ii) the scientific and educational gaps emerging from reduced dependence on animal models, and (iii) the role of AI and deep learning in reconstructing biological complexity through multi-omics integration and predictive toxicity modeling. By analyzing emerging AI platforms and computational strategies, this review highlights how AI-driven polypharmacology may offer a scalable, ethical, and precision-oriented framework for future pharmacological research.

Drug discovery is an exceptionally long and costly process, often taking over 10 years and costing billions of dollars. Despite these efforts, more than 90% of drug candidates fail, with most failures occurring during clinical trials due to issues related to efficacy, safety, or poor pharmacokinetics. A major contributor to these failures is biopharmaceutic barriers, including poor solubility, limited permeability, active efflux by transporters such as P-glycoprotein and breast cancer resistance protein, and extensive first-pass metabolism by CYP450 enzymes. These factors severely limit drug absorption and bioavailability, reducing therapeutic efficacy. Although traditional approaches, such as high-throughput absorption, distribution, metabolism, and excretion screening and improved chemical design, have achieved some progress, a major shift is now occurring through the use of in silico modeling, artificial intelligence (AI), and machine learning. These AI-driven tools enhance the prediction accuracy of absorption, distribution, metabolism, and excretion profiles, identify transporter interactions, and even simulate metabolic pathways. Additionally, modern formulation technologies, such as three-dimensional printing, lipid-based nanocarriers, and biodegradable delivery systems, are increasingly being integrated with AI-powered design platforms to personalize and optimize drug delivery. However, these promising advancements also raise regulatory and ethical concerns that must be addressed before widespread adoption. This review examines the major biopharmaceutic barriers responsible for drug development failures and explores how emerging AI-driven strategies and formulation innovations are being used to overcome these limitations. It also discusses current regulatory challenges and ethical considerations associated with adopting these technologies.

Ischemic stroke is a complex cerebrovascular disorder characterized by highly unpredictable outcomes influenced by patient-specific variables, including age, stroke severity, and preventable stroke-related complications such as infections. Analyses of clinical data have indicated a cumulative post-stroke infection rate of approximately 30%, with reported rates ranging from 5% to 65%. Post-stroke infections pose a significant challenge, as they not only increase the financial burden of stroke care but are also associated with adverse clinical outcomes, prolonged hospital stays, and a higher risk of stroke recurrence. The inflammatory response plays a pivotal role in the pathophysiology of ischemic stroke, encompassing the activation of inflammatory cells, the release of inflammatory mediators, and the engagement of inflammatory signaling pathways. Recent advances in molecular biology have facilitated the identification and investigation of numerous inflammation-related biomarkers. This article reviews the roles and mechanisms of key inflammatory biomarkers, including cytokines, chemokines, adhesion molecules, inflammation-related enzymes and mediators, receptors, signaling pathway molecules, and acute-phase proteins in the context of ischemic stroke, highlighting their significance in stroke pathophysiology and prognostic assessment. Additionally, in conjunction with the latest research advances, the article discusses novel biomarkers such as microRNAs and galectin-3, which are emerging as important tools in multiple domains, including diagnosis and treatment. Drawing on clinical diagnostic and therapeutic practices, this review analyzes the diagnostic and therapeutic roles of both novel and traditional biomarkers in the progression of ischemic stroke, following the temporal sequence from disease onset to prognosis. Finally, the article addresses the limitations of current research and offers perspectives on future directions, providing insights that may contribute to the advancement of precision medicine in the management of ischemic stroke.

Cortisol, the body’s primary glucocorticoid, is central to maintaining homeostasis through its regulation of metabolism, immunity, cardiovascular tone, and neurobehavioral functions. However, chronic dysregulation of the hypothalamic–pituitary–adrenal axis, whether from persistent psychological stress, lifestyle imbalance, or circadian disruption, contributes to diverse metabolic, psychiatric, and inflammatory disorders. This comprehensive review aims to explore cortisol physiology, mechanisms of dysregulation, and emerging strategies for restoring hormonal balance through integrative management. Conventional approaches such as pharmacotherapy and surgical interventions remain essential for severe endocrine disorders like Cushing’s syndrome and Addison’s disease; however, they inadequately address chronic, stress-related dysfunction. Nutritional modulation, sleep optimization, moderate physical activity, and mind-body therapies, including yoga, meditation, and mindfulness-based stress reduction, demonstrate measurable reductions in cortisol and inflammatory cytokines. Adaptogenic botanicals such as Withania somnifera, Ocimum tenuiflorum, Rhodiola rosea, and Panax ginseng exhibit robust evidence for normalizing cortisol and enhancing resilience through hypothalamic–pituitary–adrenal axis modulation. Complementary modalities such as acupuncture, naturopathy, and homeopathy show potential in improving autonomic and neuroendocrine balance. By synthesizing biomedical, nutritional, psychological, and traditional perspectives, this review proposes an integrated model of cortisol management that harmonizes physiology and behavior. Such multidimensional frameworks offer promising, evidence-based pathways for mitigating stress-related diseases and promoting holistic well-being.

Precision medicine represents a paradigm shift in healthcare, emphasizing individualized approaches to disease prevention, diagnosis, and treatment based on a patient’s genetic, proteomic, and immunologic profile. In the field of oncology, this paradigm has gained traction, particularly with the integration of immunotherapeutic modalities. Among the most promising advancements are therapeutic cancer vaccines, which harness the body’s immune system to fight tumors more effectively. This mini-review highlights recent developments in therapeutic vaccine engineering. It also discusses key barriers to clinical translation and summarizes findings from contemporary human clinical trials evaluating personalized cancer vaccines. In addition, it evaluates the growing potential of these therapies to redefine cancer treatment.

Breast cancer remains one of the most common cancers affecting women globally, with late detection frequently contributing to its high mortality rate. Multiple factors drive these delays, including a lack of awareness, financial constraints in low-income countries, and limited access to non-invasive and accurate biomarkers. This review aims to introduce biomarkers, particularly hematological and biochemical serum markers, as essential, non-invasive, and accurate tools for improving the diagnosis, prognosis, and therapeutic management of breast cancer. Hematological markers are measurable blood parameters that reflect physiological and pathological processes such as inflammation, infection, cardiovascular stress, autoimmune conditions, and cancer. Routinely measured hematological markers, such as the neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and red blood cell indices, are typically obtained from standard tests like the complete blood count. Regular monitoring through complete blood count is essential during cancer treatment to evaluate changes in blood cell counts and detect potential adverse effects. Because of their affordability, minimal infrastructure requirements, and broad accessibility, hematological parameters have been increasingly studied for their association with high-risk factors in breast cancer, particularly in resource-limited settings. Their utility underscores their critical role in improving patient outcomes across diverse healthcare environments. This review summarizes the clinical value of various hematological and serum-based biochemical markers in the screening and diagnosis of breast cancer. Prediction methods that incorporate hematological and serum-based biochemical parameters can support screening, diagnosis, and staging. Overall, individual or combined blood indicators hold significant potential to enhance diagnostic accuracy and effectiveness.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has become one of the leading causes of chronic liver diseases in China, imposing a substantial and growing burden on the healthcare system. Considering the large number of individuals affected by MAFLD and the gap in disease management capacity at the primary care level, standardized guidance tailored to primary healthcare settings is urgently needed. In response, the Chronic Disease Management Branch of the China Medical Biotechnology Association convened a multidisciplinary working group incorporating hepatologists, general practitioners, and other specialists to initiate the first China national Guidelines for Diagnosis and Management of Metabolic Dysfunction-associated Fatty Liver Disease in Primary Care (2025). These guidelines provide recommendations and suggestions covering screening, risk assessment, diagnosis, treatment, referral pathways, and follow-up tailored for primary care institutions, thereby improving the long-term outcomes for the population with MAFLD and comprehensively strengthening the role of primary healthcare in chronic liver disease management.