搜索结果：revolutionize

Precision therapies such as monoclonal antibodies (mAbs), immune-checkpoint inhibitors (ICIs), and chimeric antigen receptor T (CAR T)-cell therapies have revolutionized the treatment of cancer. Of these, bispecific antibodies (bsAbs) are gaining attention for their ability to simultaneously target two distinct molecular antigens, aimed at engaging T cells to circumvent cancer immune evasion mechanisms, resulting in tumor cytolysis. Several bsAbs have now been approved in the United States, Europe, and Japan for the treatment of hematological malignancies, most of which are CD3-redirecting bsAbs, although other immune-activating mechanisms are also being explored. This chapter reviews bsAbs in clinical development for hematological malignancies, their basic structure and mechanism of action, and efficacy and safety results of the most advanced bsAbs under clinical investigation.

Exosomes, a specialized group of extracellular vesicles (EVs) ranging from 30 to 150 nm in size, have emerged as important mediators of intercellular communication across both mammalian and plant kingdoms. By facilitating the transfer of bioactive cargo, these vesicles orchestrate complex signalling networks essential for physiological homeostasis and pathological progression. Extensive research into both plant and mammalian exosomes has established their inherent biocompatibility and potential as cell-free systems for the targeted delivery of therapeutic drugs in the management of various diseases, including cancer. Moreover, exosomes have unique molecular signatures that enable the identification of their cell of origin and serve as high-fidelity biomarkers for precise disease diagnosis. This review provides a comprehensive overview of exosomal biology, beginning with the critical importance of standardized isolation and enrichment protocols to ensure sample purity and yield. This review also evaluates contemporary detection and characterization methodologies, ranging from nanoparticle tracking analysis to super-resolution microscopy, which are essential for defining exosomal identity. Moreover, the dual role of exosomes in cancer and disease progression, emphasizing their ability to modulate the tumour microenvironment and serve as non-invasive biomarkers for liquid biopsies, has also been explored. Bioengineering strategies such as genetic manipulation of the host cell or chemical modification of the exosome surface, can enhance their targeting precision and therapeutic loading capacity. The review also discusses the capacity of these vesicles to be loaded with exogenous drugs, including small molecules and RNA-based therapeutics, transforming them into potent, site-specific delivery vehicles. Despite their immense potential as biocompatible, cell-free drug delivery vehicles, several translational hurdles, including large-scale manufacturing consistency and regulatory complexities, remain as significant challenges, which if addressed, can lead to a shift toward personalized exosome-based diagnosis and therapy that could revolutionize the management of cancer and other complex systemic disorders.

messenger RNA-based therapeutics have revolutionized the treatment and prevention of infectious, neurological, and cancer diseases. However, their linear topology makes them susceptible to rapid degradation in vivo, which limits their therapeutic efficacy. Engineered circular RNAs (circRNAs) due to their closed ends and high stability are emerging as a promising alternative to linear RNA therapies. Engineered circRNAs are also increasingly used to mimic naturally occurring circRNAs in functional studies. Both applications, however, depend on production of precise circRNAs with homogenous sequences to enable accurate interpretation of biological outcomes. To address this, we developed and optimized methods for generating precise circRNAs. We employed enzymatic ligation of linear RNAs rather than autocatalytic splicing to produce circRNAs to minimize extraneous nucleotides remaining from the ribozymes. We carefully designed the DNA transcription template to maintain sequence and structural integrity. A permuted DNA template leveraging three internal guanosines (Gs) was synthesized and amplified using a reverse primer containing two 2'-O-methyl groups. This approach optimally produced the linear precursor RNA with correct 5' and 3' ends. After testing multiple workflows, we found that GMP-primed in vitro transcription, T4 RNA ligase 2-mediated circularization, and urea-polyacrylamide gel electrophoresis (PAGE) gel extraction produced the highest fidelity circRNAs.

Monoclonal antibodies (mAbs), as biologics and biosimilars, have revolutionized patient care for certain chronic inflammatory and oncological conditions over the past decade. Immunogenicity against these biologic therapeutics is not uncommon and can be associated with adverse reactions, attenuated efficacy, or altered pharmacokinetics (PK). Concerns have been voiced about whether biosimilars are immunologically equivalent to their respective biologics, highlighting the risk of clinically meaningful adverse differential immunogenicity. However, integrating evidence from well-controlled clinical trials, health authority reviews, and real-world monitoring of over 61 biosimilar mAbs of 13 reference biologic mAbs, and accounting for human immune biology, does not support the concerns raised.

Thrombosis is a multifaceted pathological process involving intravascular clot formation that drives a range of cardiovascular and cerebrovascular diseases, including myocardial infarction, ischemic stroke, venous thromboembolism, and cancer-associated thrombosis. Despite major advances in anticoagulation and thrombolytic therapies, clinical outcomes remain limited by bleeding risk, incomplete clot dissolution, and variable patient response. Emerging evidence increasingly portrays thrombosis as a dynamic immunoinflammatory disorder, integrating coagulation, inflammation, and immune signaling into what is collectively termed thrombo-inflammation. Mechanistic insights have uncovered crucial roles for neutrophil extracellular traps (NETs), platelet-leukocyte interactions, complement activation, and metabolites derived from gut microbiota in facilitating the formation and persistence of thrombi. Translational research has mirrored this trend, with the development of innovative therapeutics such as Factor XI/XII inhibitors, RNA-based agents, and nanocarrier-guided delivery systems. These are crafted to precisely modulate antithrombotic efficacy while ensuring the preservation of hemostatic balance. At the same time, advancements in thrombolytic techniques, such as clot-sensitive fibrinolytics and interventions using ultrasound assistance and catheter guidance, provide treatment options focused on precision. Advances in diagnostic platforms, including artificial intelligence (AI) -enhanced imaging, microfluidic assays, and multi-omics biomarker profiling, are further transforming early detection and individualized risk stratification. This review brings together current molecular and translational insights to map out the evolving paradigm of thrombosis as a systemic, multifactorial process. It emphasizes the emerging connections among bioengineering, omics-based profiling, and artificial intelligence, showcasing their combined potential to revolutionize prevention, diagnosis, and treatment strategies. By integrating these multidimensional perspectives, this review aims to chart the path toward precision thrombosis medicine and mechanism-targeted therapeutic innovation.

Traditional auscultation, heavily dependent on the subjective judgment of physicians, can lead to variability in diagnoses. This study aimed to explore the application of machine learning algorithms for analyzing breath sounds in children with asthma, particularly those with asthma in remission and those with cough variant asthma (CVA). Our study collected breath sound data from 50 children with asthma (30 with asthma in remission and 20 with CVA). First, we preprocessed and extracted the breath sound data. Second, machine learning techniques were applied to objectively classify and evaluate the breath sounds of pediatric asthma patients. Then logistic regression, random forest, and support vector machine algorithms were employed to train models and predict outcomes. In this study, the support vector machine achieved the best performance in distinguishing between breath sounds from children with asthma in remission and those with CVA. It reached an accuracy of 98.32%, a sensitivity of 96.23%, and an area under the receiver operating characteristic curve of 0.99 in predicting pediatric asthma subtypes. Our findings highlight the potential of machine learning models to revolutionize the diagnosis and treatment of pediatric asthma, offering a pathway towards more precise and individualized therapeutic strategies. ClinicalTrials.gov number: ChiCTR2300077717 (Registration Date: 2023-11-16).

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of diverse solid tumors; however, concurrent viral infections significantly influence their efficacy and safety profiles. By driving persistent antigen exposure, inducing T cell exhaustion, and remodeling the immunosuppressive tumor microenvironment (TME), viruses extensively reconfigure tumor immune landscapes, leading to marked heterogeneity in responses to immunotherapy. Emerging evidence indicates that patients infected with hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), human papillomavirus (HPV), or Epstein-Barr virus (EBV) who receive ICIs therapy may not only regain antitumor immune function, but in some cases may also be associated with virological responses, immunological changes suggestive of improved viral control. However, the risk of viral reactivation remains a concern, particularly in the context of immune-related adverse events (irAEs) requiring immunosuppressive treatment. This review systematically summarizes the current clinical application of ICIs across different viral infection backgrounds and highlights recent advances in the underlying immunological mechanisms. Furthermore, we propose the potential value of virus-specific immune profiling in guiding individualized treatment strategies and emphasize the need to optimize the integration of ICIs and antiviral therapies from the perspective of systemic immune reprogramming.

Super enhancers (SEs) are crucial regulatory elements in the genome that determine cell identity and fate commitment through mechanisms such as recruiting transcription factors (TFs), interacting with super enhancers RNAs (seRNAs), and phase separation. This ensures accurate gene expression in response to both environmental and intrinsic signals. Advances in 3D genome architecture have further illuminated how SEs are organized and regulated within the nucleus, thereby enhancing our understanding of their function. Recent innovations in genomic technologies, including chromatin accessibility, CRISPR-Cas9, 3 C-based methods and imaging methods, have revolutionized the study of SEs. These technologies have facilitated precise mapping and functional characterization, providing deeper insights into how SEs influence gene regulation. In particular, these advancements offer promising potential for exploring SE dynamics in livestock improvement, opening avenues for the enhancement of agricultural and breeding practices.

The development of hybridoma technology by Köhler and Milstein revolutionized the field of antibody research, paving the way for the creation of monoclonal antibodies. Due to their ability to specifically bind target molecules via antigen-antibody reactions, antibodies have become a major focus in therapeutic development. However, antibodies obtained from immunized animals such as mice may cause immunogenicity or side effects when administered to humans. While humanized mice offer a solution, their high-cost limits widespread adoption. Advances in genetic engineering technology have made it possible to create chimeric antibodies or humanized antibodies and have greatly reduced immunogenicity. In this chapter, we explain in detail a laboratory-scale protocol to produce recombinant IgG antibodies at the milligram scale, which is essential for in vivo research, using transient expression in CHO cells.

Analytical validation is a crucial step in the evaluation of algorithms that process data from sensor-based digital health technologies (sDHTs). Analytical validation of novel digital measures can be complicated when reference measures with directly comparable units are not available. To address this, we conducted a simulation study. Data was simulated assuming a latent physical ability trait, indirectly accessed through an sDHT-derived target measure collecting step count data, and the items of a clinical outcome assessment (COA) measuring self-reported physical activity. We quantified the ability of two methods to assess the latent relationship between reference and target measures: the Pearson Correlation Coefficient (PCC) and factor correlations from a two-factor confirmatory factor analysis (CFA) model. Additionally, three multiple linear regression models were used to evaluate if multiple COA reference measures can more completely represent a target measure of interest. Our findings show that PCC was more stable, easier to compute, and relatively robust with respect to violations of parametric assumptions than CFA, particularly with small sample sizes. However, CFA was less biased than PCC in all scenarios investigated. We demonstrate that using both PCC and CFA generates more confidence in the results of a target and reference measure comparison. Finally, regression results suggest that incorporating multiple reference measures with more frequent collection time points can provide a more complete presentation of the sDHT's analytical validity. Novel digital measures are being developed at an accelerating pace and promise to revolutionize patient care and medical product development. Our findings provide investigators with crucial information for choosing appropriate methods to perform rigorous analytical validation of these novel measures, including an open-access simulation toolkit.

Immunotherapy has revolutionized oncology by harnessing the host immune system to combat malignancies, yet immune-related adverse events (irAEs) present formidable challenges to treatment sustainability and patient outcomes. This review systematically analyzes the mechanisms of irAEs induced by key immunotherapies, including immune checkpoint inhibitors (ICIs), chimeric antigen receptor T (CAR-T) cell therapy, cancer vaccines, and oncolytic viruses (OVs), with a focus on organ-specific manifestations. While current clinical management relies heavily on corticosteroids and immunosuppressive agents, prolonged use risks infections and diminished antitumor responses. Recent preclinical advances highlight innovative strategies to mitigate irAEs while preserving therapeutic activity, such as tumor-targeted drug delivery systems, cytokine pathway inhibitors, and certain indirect regulating protocols. Combinatorial approaches, including ICIs incorporated with chemotherapy or anti-angiogenic agents and traditional Chinese medicine interventions, have also demonstrated synergistic potential. Future directions emphasize elucidating molecular drivers of irAEs, developing predictive biomarkers, and refining targeted delivery technologies to balance efficacy and safety. This review underscores the critical role of interdisciplinary collaboration in advancing next-generation immunotherapies, offering actionable insights for optimizing clinical practice and translational research.

The emergence of advanced genome editing technologies has revolutionized research in life sciences, offering an unprecedented way to uncover unknown biological functions and innovative therapeutic strategies. Among all genome editing tools, CRISPR-Cas-based technologies play a pivotal role in this revolution, particularly Class 2 effectors such as Cas9 and Cas12, owing to their high efficacy and ease of programmability. With the advancements in genome sequencing and metagenomics, an increasing number of novel CRISPR-Cas systems have been discovered, including those found in extreme environments and viruses. Furthermore, recent studies have revealed an unexpected role of non-Cas accessory genes, such as the Tn7-like transposon and Pro-CRISPR factors (Pcr), in conferring additional functionalities to the CRISPR system, providing new insights into the understanding of CRISPR-mediated bacterial immunity and advancing the development of genome editing technologies. Therefore, it is essential to develop comprehensive methods for characterizing the Cas proteins and Pro-CRISPR factors with a growing diversity. In this protocol, we provide a method encompassing protein purification, biochemical characterization, validation of protein-protein interactions, and preliminary in vivo functional assays in bacteria for Cas nuclease and its associated Pro-CRISPR factor. We hope this protocol will not only assist in the characterization of the CRISPR-Cas system, but also provide valuable guidance for the characterization of other nucleases or nucleic acid modification systems.

Cardiovascular diseases remain the leading cause of mortality globally and in Italy, with a growing burden exacerbated by ageing populations and underdeveloped strategies for managing chronic cardiovascular conditions. This position paper, resulting from the 2024 ANMCO General Assembly, addresses the current state of cardiovascular chronicity management in Italy, highlighting critical gaps and proposing sustainable, integrative solutions. Despite improvements in acute cardiovascular care, the lack of structured post-acute management, insufficient adoption of secondary prevention protocols, limited access to innovative therapies, and a slow digital transition continue to hinder effective chronic care. The document stresses the pivotal role of cardiologists, not only in acute intervention but also in long-term care and secondary prevention, emphasising the need for a multidisciplinary, multichannel healthcare model. The paper explores the potential of e-Health and artificial intelligence to revolutionize chronic disease management. It advocates for the widespread implementation of integrated care pathways, digital tools like electronic health records and telemedicine platforms, which together could enhance early detection, patient monitoring, and therapeutic adherence while reducing unnecessary hospitalisations. It also underscores the necessity of updating national and regional pharmaceutical policies to improve equitable access to disease-modifying therapies. Furthermore, the integration of palliative care in end-stage cardiovascular disease and the enhancement of post-acute care networks are deemed essential. Ultimately, the document advocates for a comprehensive systemic and cultural transformation spearheaded by scientific societies such as ANMCO, where technological innovation, organisational reform, and patient-centred care align to ensure a sustainable and universally accessible healthcare system. This vision is consistent with the objectives of the PNRR, the 2030 Agenda, and, most importantly, the foundational principles of the Italian Constitution.

Molecular profiling has revolutionized the diagnosis and management of patients with non-small cell lung cancer (NSCLC), enabling personalized treatment approaches that have significantly improved patient outcomes. Identifying specific gene mutations and protein expression profiles guides therapeutic decisions and has become standard in the management of patients with NSCLC. Multiple organizations have published guidelines for biomarker testing. However, differences in the timing, methodology, and scope of recommended targets create potential for inconsistencies in clinical practice. This article reviews the clinical importance of biomarker testing in NSCLC, compares major guidelines, and highlights the challenges and implications of guideline variability in formulating recommendations on biomarker testing for patients with NSCLC. In working to improve care for patients with lung cancer, the American Cancer Society (ACS) National Lung Cancer Roundtable supports comprehensive testing for biomarkers (checking for special changes or signals in the cancer) as essential for determining appropriate treatment options for all eligible patients with non‐small cell lung cancer (NSCLC). Many factors lead to some patients not receiving timely, optimal biomarker testing. The ACS National Lung Cancer Roundtable held a collaborative summit and developed a strategic plan to promote comprehensive biomarker testing for all patients with NSCLC. These plans included addressing similarities, differences, and gaps in recommendations for comprehensive biomarker testing in NSCLC clinical treatment guidelines.

Acute myeloid leukemia (AML) is a heterogeneous malignancy, with clonal complexity and somatic mutations critically influencing prognosis and treatment. While global genomic profiling efforts have revolutionized AML classification and risk stratification, the molecular landscape in Pakistani patients remains underexplored. Our aim is to perform targeted next-generation sequencing (NGS) for somatic mutational profiling of newly diagnosed AML patients in Pakistan. This prospective study was conducted at the Armed Forces Institute of Pathology, Pakistan, from January 2021 to January 2026. Among 104 patients, 204 somatic variants were identified (mean: 1.96 variants/patient), predominantly single-nucleotide variants (49.5%). Missense mutations (38.2%) were most common, with enriched transitions (Ti/Tv: 1.27:1). Frequently mutated genes included TP53 (22.1%), KIT (9.8%), CEBPA (8.8%), and NRAS (5.9%). Cell-signaling genes (30.4%) and tumor suppressor genes (27.0%) were the most affected functional groups. Co-mutation analysis showed clustering led by DNMT3A-IDH1 co-occurrence (ρ ≈ 0.43). DNA-methylation alterations frequently co-occurred with tumor suppressors (OR ≈ 4.6, p = 0.007), transcription factors (OR ≈ 3.9, p = 0.023), and NPM1 (ρ = 0.32). This study provides the first comprehensive genomic map of Pakistani AML patients, revealing unique mutational signatures. The findings lay the groundwork for population-specific precision oncology in low- and middle-income countries.

Although Arsenic trioxide (ATO) has revolutionized acute promyelocytic leukaemia (APL) from a uniformly fatal disease to one of the most curable leukemias, leukocytosis is a common life-threatening side effect and is associated with inferior results. This study aimed to describe leukocyte proliferation kinetics and explore predictors of leukocytosis among APL patients in order to guide individualized application of ATO. In this retrospective study of 100 patients, the incidence of leukocytosis in the single drug ATO group was 74.5%, while that in the ATO- chemotherapy group was 93.9% (P = 0.018). The dynamic changes in leukocytosis showed a higher WBC count in the ATO-chemotherapy group. The median time to leukocytosis was the 9th day (IQR, 6.00-14.00). The duration of leukocytosis was shorter in the ATO single agent group. The uni-variate analysis and multi-variable analysis showed that the same independent prognostic factor was the time to double WBC for leukocytosis for all patients (OR 11.459, 95% CI, 2.121-61.897) and ATO single agent patients (OR 16.603, 95% CI, 1.635-168.635). The area under the ROC curve was 0.835 (95% CI, 0.700-0.971) for all APL patients and 0.822 (95% CI, 0.675-0.969) for single ATO-induced leukocytosis. The kinetics of leukocytosis induced by single-agent ATO and ATO-chemotherapy were different. The time to double WBC≤7th day was identified as an independent poor prognostic factor for single ATO-induced leukocytosis in APL patients.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer therapy by reinvigorating antitumor immunity through the blockade of inhibitory pathways such as programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). Despite their remarkable clinical success, only a subset of patients derives durable benefit, whereas others exhibit primary or acquired resistance and develop immune-related adverse events (irAEs). These heterogeneous responses highlight an urgent need for robust biomarkers to predict therapeutic efficacy and for innovative combinatorial strategies to enhance clinical outcomes. Beyond their classical roles in hemostasis and thrombosis, platelets have recently emerged as pivotal modulators of tumor progression and immune regulation. Accumulating evidence indicates that platelets engage in dynamic crosstalk with tumor and immune cells, reshaping the tumor microenvironment (TME) and modulating the response to ICI therapy. Of note, platelet-associated immune checkpoint molecules (e.g., PD-L1) have shown great promise as liquid biopsy markers for patient stratification and real-time immunomonitoring. Furthermore, platelet-associated nucleic acids and traditional platelet parameters (such as platelet count and activation status) have been identified as accessible and effective biomarkers for predicting ICI responsiveness and irAEs. These platelet-derived components may also represent novel therapeutic targets to overcome resistance and potentiate ICI efficacy. Meanwhile, advances in biomaterials and genetic engineering have further enabled the development of platelet-based and platelet membrane (PM)-camouflaged delivery systems endowed with tumor-homing capacity, combinatorial drug delivery potential, and immune-responsive release properties. Collectively, these insights reposition platelets from passive participants to active regulators and versatile therapeutic platforms in cancer immunotherapy, providing a conceptual foundation for next-generation platelet-guided precision immunotherapeutic strategies.

Large language models, such as OpenAI's ChatGPT, have the potential to revolutionize patient education. These platforms allow for a vast collection, analysis, and organization of information largely unavailable to online users. Within medicine, these tools could help complement physicians in better educating patients on complex and routine medical information. Currently, limited literature exists on the reliability of such tools to provide high-quality information to patients inquiring about gender-affirming top surgery. Therefore, this study aimed to evaluate ChatGPT's performance when generating patient-level information on gender-affirming top surgery compared with the current online information provided by the American Society of Plastic Surgery (ASPS) using the Modified Ensuring Quality Information for Patients (mEQIP) tool. ChatGPT-4-generated patient-level education on transmasculine and transfeminine gender-affirming top surgery was compared against current online content provided by the ASPS. ChatGPT-4 patient content was generated by individually formatting standardized mEQIP content items to incorporate the topic of gender-affirming top surgery into ChatGPT-4, with responses recorded for each item. Four experts in gender-affirming top surgery independently rated both sources using a 36-item mEQIP tool. Paired t-tests comparing overall and content-specific mEQIP scores of the ChatGPT-4 and ASPS material were then estimated to measure the quality of the content. The effect size between the two groups was evaluated using Cohen's d. Lastly, Cronbach's alpha and ICC (Intraclass Correlation Coefficient) were calculated to measure internal consistency among raters and interrater agreement. When analyzing ChatGPT-4 and ASPS patient material, paired t-tests showed a statistically significant increase in overall mEQIP scores for ChatGPT with a mean difference of 7.50 (CI 6.75-8.25; p<0.001). For the mEQIP content-specific scores, a paired t-test revealed a similarly significant increase in ChatGPT scores with a mean difference of 9.75 (CI 9.26-10.24; p<0.001). When evaluating the effect size, a paired Cohen's d value of 13.00 was calculated, demonstrating a statistically significant difference in magnitude between the two groups. To measure internal consistency among raters and interrater agreement, an ICC&#xa0;and Cronbach's alpha were calculated for both ASPS and ChatGPT. The ASPS-mEQIP showed good internal consistency and excellent interrater reliability (ICC=0.89, Cronbach's &#x3b1;=0.84), while ChatGPT-mEQIP showed excellent internal consistency and excellent interrater reliability (ICC=0.96, Cronbach's &#x3b1;=0.96). These results demonstrate that ChatGPT-4-generated patient education on gender-affirming top surgery exceeded the current ASPS online content in both overall and content-specific scores, as measured by the mEQIP tool. ChatGPT achieved significantly higher scores across both domains with large effect sizes, and raters demonstrated excellent internal consistency and excellent interrater reliability. Going forward, commonly accessible artificial Intelligences (AIs), such as ChatGPT, may serve as a valuable complement to patient education and shared decision-making within plastic and reconstructive surgery, though future studies are warranted to evaluate freely generated responses to better reflect current AI use.

SignificanceThe review aims to systematically explore the transformative impact of artificial intelligence (AI) on pharmaceutical sciences. It Address key research questions regarding how AI accelerates drug development, enhances clinical trial design, optimizes manufacturing, and drives advances in personalized and precision medicine.MethodsA comprehensive literature review was conducted. It Synthesized recent studies, industry reports, and regulatory guidelines on AI adoption. Covered drug discovery, clinical trials, pharmacovigilance, manufacturing, supply chain management, and pharmacy education. It also critically examined barriers such as data quality, privacy, explainability, and the evolving regulatory landscape.ResultsAI accelerates pharmaceutical R&, identification, lead optimization, ADMET prediction, drug repurposing, and clinical trial analytics. It enables faster and more cost-effective development. Advancements in personalized medicine and individualized patient data are driving better patient outcomes. The students still ace continuing challenges in data interpretation, privacy, and regulatory issues.ConclusionsOngoing AI advancements and evolving regulations are set to revolutionize pharmaceutical science. They will enable efficient, predictive, and patient-centered healthcare. Success will depend on integrating multi-omics, adopting explainable AI, and fostering collaboration among all stakeholders. Ultimately, AI promises safer, faster, and more precise drug delivery system. It benefits clinicians, researchers, and students, and students.