Search Results (1,180)

Background/Objectives: Migraine is a neurological disorder with a heterogeneous presentation; however, compounds perceived as effective by migraine sufferers are often underrepresented in research. This study adopts a novel exploratory approach to determine whether pharmacodynamic data from such compounds can be used to prioritise candidate migraine-related genes and biological pathways for future investigation, and to generate hypotheses regarding migraine pathophysiology. Methods: A structured internet search identified 181 compounds perceived as effective for migraine, of which 148 had at least one supportive report identified in the PubMed literature. Following DrugBank-based exclusions for insufficient interaction data, 137 compounds remained for analysis. A gene list was extrapolated from compound–protein interactions, followed by analyses identifying significantly enriched tissues and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Unique genes from enriched tissue-specific sublists were considered targets of interest, and those not previously directly associated with migraine in the databases and literature searches used were deemed candidate migraine target genes. Results: A total of 32 compounds affected the products of 21 targets of interest. CHRM3, CHRNA4, and SCN2B emerged as prioritised candidate genes that had not previously been directly associated with migraine in the databases examined. Enrichment analysis of the pharmacodynamically derived gene set identified tissue-enrichment signals involving skeletal muscle (q = 6.01 × 10⁻⁵), prefrontal cortex (q = 5.36 × 10⁻³), cerebellar peduncles (q = 1.28 × 10⁻²) and cerebellum (q = 4.79 × 10⁻²). Nine compounds interacting with established Familial Hemiplegic Migraine (FHM) genes may warrant future investigation. No conclusions regarding therapeutic efficacy are drawn from these data. Conclusions: This exploratory pipeline prioritised CHRM3, CHRNA4, and SCN2B as candidate genes for further investigation, alongside enriched tissues and pathways. These findings should be regarded as hypothesis-generating rather than evidence of causality or therapeutic utility. More broadly, variations of this prioritisation framework may prove useful for exploring other disorders. Full article

Objectives: Epilepsy is a chronic neurological disorder associated with substantial morbidity, reduced quality of life, and a considerable global healthcare burden. We aimed to assess anti-seizure medication (ASMS) utilization patterns, adherence levels, and factors influencing seizure control and adverse effects among pediatric patients with epilepsy in Oman. Methods: A retrospective cross-sectional study was conducted at Sultan Qaboos University Hospital (SQUH) between January 2023 and November 2024. A total of 305 pediatric patients aged 2–12 years receiving at least one ASMS with a minimum of two years of follow-up were included. Data were extracted from electronic medical records. Adherence was assessed using the Medication Possession Ratio (MPR). Statistical analysis was performed using SPSS. Results: Levetiracetam (68.52%) was the most prescribed ASMS, followed by sodium valproate (22.62%) and topiramate (8.52%). Adherence was observed in 54.68% of patients. Mean seizure duration decreased progressively across follow-up visits. Seizure control was achieved in 43% of patients. Patients without adverse effects had significantly better seizure control (56.2% vs. 10.2%, p < 0.001). Dose increases were associated with higher rates of adverse effects. Conclusions: Levetiracetam was the most commonly prescribed ASMS. Seizure type influenced drug selection. Adverse effects had a strong negative impact on seizure control, while adherence showed a limited association, indicating that managing side effects is crucial for effective treatment. Optimizing ASMS dosing and minimizing adverse effects are essential to improving outcomes in pediatric epilepsy. Dose adjustments, particularly dose increases, were associated with a higher frequency of adverse effects. Careful dose optimization may therefore be important to balance tolerability and seizure management. In this study, just over half of the patients evaluated (54.68%) were classified as adherent based on Medication Possession Ratio. Full article

Diabetes mellitus is a long-term metabolic disease associated with elevated glucose levels in blood and still constitutes one of the major public health issues worldwide. Inhibition of carbohydrate-digesting enzymes like α-amylase and α-glucosidase has been found to be effective in controlling postprandial hyperglycemia. The current study focused on designing, synthesis, characterization, and evaluation of novel 2,4-thiazolidinedione derivatives (D1–D5) as potent antidiabetic drugs utilizing combined in silico, in vitro, and in vivo techniques. Results from drug-likeness and ADME analyses indicated that all synthesized derivatives met Lipinski’s rule of five and had desirable pharmacokinetics properties along with reduced toxicity. Molecular docking against maltase-glucoamylase (human; PDB ID: 3TOP) protein showed good binding affinities of both D1 and D5 derivatives (−7.74 and −7.40 kcal/mol respectively) due to stable interactions with catalytic residues of enzymes. Inhibition of enzymes in vitro showed that D1 and D5 had the highest inhibitory activities of all synthesized derivatives, with IC₅₀ of 33.86 ± 2.1 and 37.55 ± 1.7 μM against α-amylase and 29.81 ± 3.2 and 32.43 ± 1.2 μM against α-glucosidase, respectively. Cytocompatibility tests on L6 myoblast cells proved that the lead compounds were well tolerated. In addition, studies in a model of Drosophila melanogaster induced by a high-sugar diet revealed a significant decrease in the level of glucose concentration depending on the dose, especially for D1 and D5, indicating their antihyperglycemic activity in vivo. Thus, these data confirm that D1 and D5 can be regarded as promising lead compounds for the development of new antidiabetics acting via inhibition of carbohydrate-metabolizing enzymes. Full article

Background: Anticoagulant treatment is central to the prevention and management of thromboembolic cardiovascular disease, and contemporary practice has increasingly shifted toward direct oral anticoagulants. Objectives: To quantify ambulatory anticoagulant utilization trends in Greece during 2018 to 2022 and to forecast utilization through 2030 using standardized drug-utilization metrics. Methods: A retrospective pharmacoepidemiological analysis of community-pharmacy anticoagulant sales data in Greece was performed using IQVIA Greece data as a proxy for population-level drug consumption. Utilization was standardized as defined daily doses per 1000 inhabitants per day. Drug-specific temporal trends were evaluated, and seasonal autoregressive integrated moving average models were developed to generate forecasts through 2030. Results: Total sales-based anticoagulant consumption increased by 31.1%, from 24.84 defined daily doses per 1000 inhabitants per day in 2018 to 32.57 in 2022, corresponding to an average annual increase of 1.57. This increase was primarily driven by direct oral anticoagulants, particularly apixaban, which increased from 5.03 ± 0.36 to 9.91 ± 0.46, and rivaroxaban, which increased from 6.84 ± 0.32 to 9.38 ± 0.29. In contrast, acenocoumarol declined by 37.6%, from 4.02 ± 0.17 to 2.51 ± 0.08. Among parenteral anticoagulants, enoxaparin increased from 1.87 ± 0.11 to 2.65 ± 0.15, whereas nadroparin decreased from 0.06 ± 0.01 to 0.02 ± 0.01. By 2030, apixaban and rivaroxaban are forecast to reach 19.61 (95% CI 18.45 to 20.77) and 9.29 (95% CI 5.09 to 13.49), respectively, whereas acenocoumarol is projected to decline to 0.97 (95% CI 0.72 to 1.23). Conclusions: Sales-based anticoagulant consumption shifted toward direct oral anticoagulants, with concurrent declines in vitamin K antagonists and selected parenteral agents, trends that may inform cardiovascular care planning, reimbursement policy, and implementation of evidence-based anticoagulation strategies. Full article

Substance use disorders (SUDs) are a leading cause of preventable death worldwide, yet approved pharmacotherapies exist for only three of them: alcohol, tobacco, and opioid use disorders. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), established in metabolic medicine, are candidates for repurposing across multiple SUDs. This review synthesizes preclinical mechanistic evidence, pharmacoepidemiologic data, and randomized trials, comprehensively for human studies and selectively for preclinical work establishing circuit-level mechanisms. GLP-1 RAs attenuate the peak amplitude and frequency of drug-evoked dopamine release in the nucleus accumbens through shared mesolimbic mechanisms while engaging substance-specific circuits, including the lateral septum for alcohol and psychostimulants and the medial habenula–interpeduncular pathway for nicotine. Evidence from CB1–incretin interaction studies suggests a distinct rationale for cannabis use disorder, in which GLP-1 RA administration may correct a drug-induced suppression of incretin tone; this framing is theoretical and has not been tested against addiction endpoints. Clinical evidence is most robust for alcohol use disorder, where convergent pharmacoepidemiologic signals and the SEMALCO randomized trial demonstrate efficacy in individuals with comorbid obesity; data for opioid, tobacco, cocaine, and cannabis use disorders remain limited. Translational barriers include dose–response relationships that remain uncharacterized because no trial has performed dose-ranging against an addiction endpoint, safety concerns in patients with eating pathology, and metabolic phenotype as a moderator, with directionally opposite effects across body mass index strata. Whether GLP-1 RAs achieve transdiagnostic utility will likely depend on patient selection and agent choice, but no validated selection criteria exist and this remains a hypothesis to be tested. Full article

Pharmaceutical-contaminated wastewater from medical facilities and households can cause ecological damage. In this study, 100% natural fruit peels, namely lime (LP), orange (OP), kiwi (KP), fig (FP), and pomegranate (PP) peels, were investigated as low-cost adsorbents for the removal of diclofenac (DCF), a non-steroidal anti-inflammatory drug (NSAID), and amoxicillin (AMX), an antibiotic, from aqueous solution. The goal of this study was to use food waste as a source for wastewater treatment. According to the results, natural fruit peels were effective for DCF removal (C₀ = 100 mg/L), with KP achieving a high removal efficiency (98.9%, 345.18 mg/g) at pH 3.0). The most promising materials (OP, KP, and FP) were further modified using MgO suspension followed by alkaline treatment with NaOH (OP-Mg, KP-Mg, and FP-Mg), resulting in enhanced performance for AMX removal (65.1%, 286.61 mg/g at pH 3.0, C₀ = 50 mg/L). The maximum short-term uptake capacity was reached within 15 min, and kinetic and isotherm models were applied to describe the adsorption data. The Langmuir expression gave the better description of the concentration dependence for both pharmaceuticals, at equilibrium for DCF and at a fixed contact time of 15 min for AMX. SEM, FTIR and XRD characterization techniques provided information on the adsorption mechanisms, as well as confirming the successful modification with Μg. In conclusion, the results demonstrate that natural fruit peels were effective in the removal of pharmaceuticals and at the same time offer potential cost and environmental advantages through waste valorization and relatively simple preparation, supporting the principles of the circular economy, through the utilization of food waste as sustainable materials for water treatment. Full article

Background: Locally advanced rectal cancer (LARC) treatment has shifted from the utilization of neoadjuvant chemoradiation (NACRT), followed by total mesorectal excision (TME) and adjuvant chemotherapy (AC) to the implementation of total neoadjuvant therapy (TNT). This transition was influenced by data from multiple studies reporting suboptimal compliance rates to adjuvant chemotherapy. As a result, a paradigm shift to TNT occurred, consisting of chemoradiation with chemotherapy, based on the RAPIDO and PRODIGE-23 studies. Methods: Following IRB approval, a single-institution retrospective chart review was conducted at a tertiary care academic institution for patients with LARC. Demographic, clinical, and treatment data were collected using REDCap. Patients were excluded if under 18 years of age, pregnant, or had stage 1 or metastatic rectal cancer. The primary objective was to evaluate TNT adherence; secondary objectives assessed adherence associations with demographic and clinical variables. Results: Two hundred twenty-nine patients with rectal cancer were initially included in the database, including 153 patients with LARC from 2019 to 2024. One hundred twenty-seven patients underwent TNT with an adherence rate of 87.5%. Statistically significant variables that contributed to adherence include male gender (p = 0.04) and non-use of recreational drugs (p = 0.04). Conclusions: This is the first real-world study assessing TNT adherence. The observed 87.5% adherence rate represents a significant improvement over historical data in which patients were treated with NACRT/TME/AC. Statistically significant variables that contributed to TNT adherence included male gender and non-use of recreational drugs. These factors may provide insight into gender differences and behavioral patterns that put patients in an at-risk category for non-adherence. Full article

POI and EOI are rare but impactful conditions among adolescents and young adults (AYAs). Little is known about the timing and incidence of mental health (MH) conditions among AYAs diagnosed with POI/EOI. Data were collected using MarketScan Medicaid administrative claims among females aged 12–25 with POI/EOI. Merative™ MarketScan^® Research Databases are large, de-identified U.S. administrative healthcare claims databases that contain individual-level information on enrollment, inpatient and outpatient medical services, and outpatient prescription drug claims. The databases include commercially insured individuals, Medicare beneficiaries with employer-sponsored supplemental coverage, and selected Medicaid populations. Claims are linked longitudinally using unique encrypted patient identifiers, allowing individuals to be followed over time across healthcare settings. In addition to healthcare utilization, the databases include demographic characteristics, diagnosis and procedure codes, dates of service, and payment information, making them well suited for epidemiologic, health services, and outcomes research. We characterized prevalence and timing of MH diagnoses, examined MH subtypes, and estimated new-onset MH among those without prior MH. Comparisons were made to demographically matched controls without POI/EOI (matched on age and calendar time) and to diagnosis-anchored comparator groups. Among 859 AYAs with POI/EOI, 53.9% had any MH diagnosis, including 32.9% with MH diagnoses before and after POI/EOI diagnosis and 11.9% with new-onset MH after POI/EOI diagnosis. Of all anxiety disorders, 60.1% were present pre- and post-diagnosis, while 25.4% were new-onset after diagnosis. Depressive disorders showed a similar pattern, with 58.1% being pre- and post-diagnosis MH group and 22.3% being new-onset. In total, 49.4% of individuals with trauma-related disorders had the diagnosis both before and after POI/EOI diagnosis, while 30.1% had it as a new-onset. In adjusted models, POI/EOI was associated with increased odds of MH diagnoses after diagnosis (aOR ~2.1, 95% CI ~1.4–3.1). Prior MH diagnoses were the strongest predictor of MH after diagnosis (aOR ~12.1). AYAs with POI/EOI experience substantial MH burden, with elevated risk of new-onset MH conditions following diagnosis. Findings highlight the importance of early MH screening and integrated care models for AYAs. Full article

This study introduces a top-down framework for evaluating the utility of multi-omics features to predict the response of 309 drugs in cancer cell lines. This was done by taking a multi-omics approach where data from proteomic, transcriptomic, genomic, metabolomic, and miRNA were integrated with drug sensitivity (area under the curve, AUC) data. We performed modular dimensionality reduction using t-SNE (t-distributed Stochastic Neighbor Embedding), followed by K-Means clustering to stratify cell lines into data-driven molecular subgroups, and applied a Random Forest model to refine the drug list, selecting only those with a prediction accuracy exceeding 75%. Our findings show that among the evaluated single-omics features, transcriptomics is the most informative; however, multi-omics integration significantly enhances predictive capability compared to single-omics analysis, with a combination of transcriptomic, proteomic, and miRNA data achieving the best predictive performance across both primary and validation datasets. Cluster analysis showed the importance of well-defined clusters, indicating that while silhouette scores were linked to prediction success, biological variability also played a critical role. This study advances personalized oncology treatment strategies and provides a foundation for future studies focused on ranking omics features based on their predictive capabilities, eventually contributing to better therapeutic outcomes. Predictive performance is used here to evaluate omics feature strength, rather than as an objective to optimize predictive models. Full article

Randomized controlled trials (RCTs) are often considered the gold standard for causal inference, but their implementation can be costly, time-consuming, and sometimes infeasible due to ethical or practical constraints. The so-called target trial emulation framework introduced the systematic use of observational data for treatment effect. This approach necessitates the detailed specification of a hypothetical trial protocol including eligibility criteria, treatment strategies, and outcome measures, which are then emulated by utilizing observational data. We expanded the target trial framework by integrating drug pathway embeddings and causal modeling, enabling prediction of treatment outcomes for unseen or held-out mechanisms of action based on the embedding relationships among existing therapies. We demonstrate that embedding-based models can reliably predict the direction of observed clinical outcomes across diverse therapeutic classes (e.g., small molecules, biologics), even when masking the observational data for the particular mechanism being estimated, though the precise magnitude of treatment effect remains hard to recover. This approach illustrates the potential to estimate the clinical efficacy of new drug mechanisms and to enhance the precision of future trial design and operations. Full article

Introduction: The increasing costs of cancer care have raised the need for economic evaluations that integrate treatment efficacy and resource utilization. This study aimed to assess the economic value of Lutetium-177 (¹⁷⁷Lu)–prostate-specific membrane antigen (PSMA)-617 in patients with PSMA-positive metastatic castration-resistant prostate cancer (mCRPC) from an Italian healthcare perspective. Patients and Methods: A cost-effectiveness analysis was performed using clinical efficacy data from randomized controlled trials (RCTs) evaluating ¹⁷⁷Lu-PSMA-617. Two scenarios were considered. Scenario 1 evaluated ¹⁷⁷Lu-PSMA-617 plus standard care (SC) versus SC alone using pivotal phase III trial data. Scenario 2 compared ¹⁷⁷Lu-PSMA-617 with chemotherapy and androgen receptor pathway inhibitors (ARPIs) using phase II and III RCTs. Drug costs were based on Italian ex-factory prices. In addition, an exploratory three-state Markov cost–utility model including progression-free survival (PFS), progressive disease, and death was developed using data from the VISION trial. Results: In Scenario 1, 730 patients were analyzed. The ESMO-MCBS grade was 3, and the incremental cost per month of PFS gained with ¹⁷⁷Lu-PSMA-617 versus SC was €20,906. In Scenario 2, 651 patients were included. The monthly cost per PFS gained with ¹⁷⁷Lu-PSMA-617 was €19,017 versus enzalutamide and €20,565 versus abiraterone. In the exploratory cost–utility model, estimated QALYs were 1.02 with ¹⁷⁷Lu-PSMA-617 and 0.64 with SC, corresponding to an incremental gain of 0.38 QALYs. At an incremental cost of approximately €32,000 per patient, the resulting model-based ICER was approximately €85,000/QALY. Conclusions:¹⁷⁷Lu-PSMA-617 provides meaningful clinical benefit in appropriately selected patients with PSMA-positive mCRPC, but this benefit is associated with substantial incremental treatment costs. The exploratory model-based ICER was sensitive to treatment duration and other model assumptions. These findings support careful assessment of the clinical value and economic sustainability of ¹⁷⁷Lu-PSMA-617 in the Italian healthcare setting. Further studies incorporating real-world resource utilization, treatment-related costs, post-progression therapies, and prospectively collected quality-of-life data are warranted. Full article

Dilute-solution properties are important sources of information on the structure of macromolecules. Analyzing experimental data and extracting information on structural properties require theoretical and computational resources. The resources needed to study rigid particles are manageable; however, studying flexible particles is more challenging. This is because, unlike rigid bodies, and as a consequence of the conformational variability arising from flexibility, flexible particles do not have a definite size and shape. In addition to their overall translational and rotational Brownian motion, the dynamics of flexible particles in solution has an internal component: size/shape conformational fluctuations. In order to facilitate the study of flexible macromolecule hydrodynamics, we have implemented existing theories within several computer programs. MONTEHYDRO combines Monte Carlo simulations based on the importance-sampling algorithm to generate conformations from which, in addition to conformational quantities, the hydrodynamic properties of flexible particles can be obtained using rigid-body treatment. SIMUFLEX is a suite based on a Brownian dynamics simulation of macromolecules, comprising BROWFLEX, for the generation of trajectories, and ANAFLEX, for the calculation of static and time-dependent properties as well as the simulation of single-particle events. In this paper, we present some concepts which are fundamental to the methods implemented in those computational tools, as well as examples of their utilization in various biomacromolecule applications, with a particular emphasis on double-stranded DNA in various cases: coarse-grained double-helical models for moderately short DNA; the worm-like model treatment of DNA over an extremely wide range of sizes (from 8 to 200,000 base pairs); and the problem of the anomalous rotational-speed dependence of the sedimentation coefficient of very long DNA. SIMUFLEX has also been particularly useful for studying intrinsically partially disordered proteins, whose structure comprises both ordered, globular domains as well as flexible tail and linker chains. To illustrate applications in the field of synthetic polymers, we describe a study on dendrimers, with aspects related to drug delivery in targeted therapies. Full article

Background/Objectives: Drug-induced sleep endoscopy (DISE) enables dynamic, real-time visualization of the level, severity, and configuration of upper airway collapse during pharmacologically induced sleep, providing anatomical information that polysomnography cannot. It is increasingly used to guide individualized surgical planning in obstructive sleep apnea (OSA), particularly in children with persistent OSA after adenotonsillectomy or at elevated risk of surgical failure. However, the absence of a universally accepted pediatric-specific classification system and standardized protocol continues to limit comparability across studies and the development of evidence-based treatment algorithms. This review evaluates the role of DISE in pediatric OSA, focusing on airway assessment, classification systems, anesthesia protocols, and its impact on surgical decision-making. Methods: A PubMed literature review (2013–2025) identified 42 pediatric-specific studies, including cohort studies, systematic reviews, and meta-analyses. Data were extracted regarding indications, classification scales, anesthetic techniques, safety, and surgical outcomes. Results: DISE provides dynamic visualization of multilevel obstructions often missed during awake examinations. While adult-derived systems such as VOTE are used, pediatric-specific tools (e.g., Chan–Parikh, NAVOTEL, PedDISE-8, IPSES) offer more age-appropriate assessments. Anesthetic choice is a key factor; dexmedetomidine, alone or with ketamine, best approximates non-rapid eye movement (NREM) sleep while maintaining airway tone and stability. DISE findings altered surgical plans in 30–60% of patients, facilitating targeted procedures such as supraglottoplasty, lingual tonsillectomy, and epiglottopexy. Conclusions: DISE is a valuable diagnostic and decision-support tool for complex pediatric OSA. Clinical utility depends on standardized assessment and anesthesia. Further multicenter studies are required to validate classification systems and determine long-term impacts on quality of life. Full article

Metastasis, chemoresistance, and tumour recurrence are facilitated by cancer stem cells (CSCs), a small subpopulation of cells capable of regenerating a primary tumour while maintaining the tumour’s genetic and phenotypic features. CSCs can be identified by the expression of specific markers; however, the CSC population in ovarian clear cell carcinoma (OCCC), a rare histotype of ovarian cancer, remains poorly defined. Given the well-established role that CSCs play in cancer progression and metastasis, it is critical to identify reliable markers of CSCs in OCCC. Here, we endeavoured to determine whether ALDH1 expression could be used to define OCCC stem cells in OCCC cell lines using a variety of methods including assessing ALDH1A1 expression in spheroids generated under distinct conditions. We also generated and used chemo-resistant cell lines to assess the enrichment of cancer stem cells. Human OCCC cell lines were enriched for CSCs using selective culture conditions and drug resistance methods. CSC-enriched spheroids demonstrated increased expression of stemness markers NANOG and SOX2, while ALDH1A1 expression was enriched only in drug-resistant cell lines, relative to parental cell lines. RNA-seq analyses of CSC-media-derived spheroids versus standard media spheroids provided novel data supporting CSC enrichment and identified transcription factors induced by CSC media. These findings highlight the ambiguous role of ALDH1A1 as a CSC marker in OCCC and demonstrates the utility of CSC enrichment methods for identifying CSC populations in OCCC cell lines. Full article

Design of experiments (DOE) offers a powerful, systematic framework for optimizing nanoparticle (NP) formulations by replacing inefficient one-factor-at-a-time (OFAT) methods. By enabling the simultaneous evaluation of multiple variables, DOE uncovers critical factor interactions and identifies true global optima—critical for quality-by-design approaches. Despite its potential for systematic innovation, DOE remains underutilized in nanomedicine due to its perceived complexity; this review provides a practical roadmap to bridge the gap between statistical theory and robust NP optimization. It provides a practical overview of DOE concepts, including factor selection, design choice, graphical interpretation of results (perturbation/contour plots), model validation (regression analysis and ANOVA), and numerical optimization via desirability function (D). Common pitfalls and best-practice strategies are discussed to support reliable model building and decision-making. A practical case study on poly(lactic-co-glycolic acid) (PLGA) NPs illustrates a multistage workflow: utilizing Taguchi screening to isolate key factors, followed by central composite design (CCD), for precise surface mapping. Numerical optimization using Design-Expert^® software maximized EE% (highest importance) within size/zeta ranges, yielding optimal conditions (5 mg drug amount, 4 mL aqueous volume; D = 0.961). Confirmation runs (EE 41.2%, NP size 124 nm, zeta potential −15 mV) validated predictions (EE 47.6%, NP size 133 nm, zeta potential −17.2 mV), confirming model reliability. Ultimately, by bridging conceptual foundations with practical implementation, this review aims to encourage broader adoption of DOE, particularly among emerging formulation scientists, and serves as a roadmap to accelerate scalable NP development, fostering data-driven innovation and improving efficiency in nanomedicine research. Moreover, future integration of artificial intelligence (AI) and artificial neural networks (ANNs) with DOE will drive a predictive, data-driven approach to NP optimization—accelerating robust, scalable, and regulatory-ready nanomedicine development with fewer experiments. Full article