With the disclosure of the important role of substructural alerts (SA) in drug development and toxicity evaluation, many automatic substructure extraction tools based on different theoretical knowledge have been reported in recent years. To compare the emphasis of various substructure extraction methods and the reliability of their results, we were encouraged to conduct a comprehensive analysis of seven representative tools to find the best one. In this paper, we introduced a well-designed evaluation of seven popular tools (Bioalerts, KRFP, MoSS, PySmash_circular, PySmash_group, PySmash_path, and SARpy) based on 43 toxicity datasets, consisting of four components: comparison of substructures derived by different methods, comparison of predictive models based on substructural rules, comparison of the efficiency of extracting toxic substructures, and the effect of SAs on quantitative structure-activity relationship (QSAR) predictive models. The results demonstrated that PySmash_circular performed best overall, with satisfactory results in substructure information carrying and the rule-based predictive models. PySmash_path and Bioalerts were also recommended for their similar performance to PySmash_circular, but the main problem was that they took too much time and generated too many substructures. Specifically, Bioalerts and PySmash_circular could obtain substructures carrying richer information, while SARpy had the best predictive rule-based models, but it only focuses on precision (PR) value in the evaluation of individual SA. More than that, the substructures obtained by all 7 methods can enhance the recognition ability of the QSAR models for toxic compounds and make them interpretable. Finally, we have also made a baseline substructure set of 43 toxicity endpoints available to the public to facilitate further development of drug research and environmental safety assessment in a rapid and accurate direction.Scientific Contribution: Based on 43 toxicity datasets, we conducted a comprehensive evaluation of 7 representative substructure extraction tools from both the perspective of individual substructure and substructure-based models. This work not only enables users to make more autonomous choices of the optimal substructure extraction tool, but also provides the public with a benchmark substructure set of 43 toxicity endpoints, promoting the further development of computational toxicology.

Interindividual variability in drug response presents a persistent challenge to effective and safe medical treatment. While conventional pharmacotherapy often relies on standardized dosing regimens, clinical outcomes frequently vary due to underlying genetic differences among patients. This paper examines personalized medicine with a focus on how genetic variation influences drug efficacy, toxicity, and therapeutic optimization. Drawing on advances in genomics, pharmacogenomics, and molecular biology, the study explores how genetic polymorphisms affect drug metabolism, transport, and target interactions. By synthesizing recent research, the paper highlights the clinical relevance of genetic biomarkers in predicting drug response and minimizing adverse drug reactions. The findings underscore the potential of personalized medicine to shift healthcare from population-based treatment paradigms toward individualized therapeutic strategies that improve patient outcomes, enhance drug safety, and support precision healthcare delivery.

Introduction MDMB-4en-PINACA is a synthetic cannabinoid receptor agonist which has been present in the recreational drug market since 2017. Its use appears to be more common within vulnerable and deprived communities. Methods This was a retrospective observational case series of patients attending emergency departments with acute drug toxicity in Birmingham, United Kingdom, between November 2020 and December 2024, who were found on routine toxicological screening to have been exposed to MDMB-4en-PINACA. Results There were 202 hospital presentations involving 163 patients with acute drug toxicity and exposure to MDMB-4en-PINACA during the study period. The majority of patients were male (82.6%); median age was 39 years (range 14–78); 32.2% were homeless. The most common clinical features were reduced level of consciousness (n = 164, 81.2%), agitation (n = 99, 49%), seizures (n = 61, 30.2%), hypotension (n = 45, 22.3%), increased blood lactate concentration (n = 95, 47%) and increased creatine kinase activity (n = 77, 38.1%). There were four deaths. In the majority of cases (n = 178, 88.1%), more than one recreational substance was detected. In 15 cases, MDMB-4en-PINACA was analytically confirmed as the only substance taken. Discussion The clinical features observed following exposure to MDMB-4en-PINACA are similar to those documented following exposure to other potent synthetic cannabinoids. Polydrug use limits the interpretation of clinical features in the majority of cases. However, the predominant clinical features in the 15 patients with no other substances detected were consistent with the overall cohort. It is likely that synthetic cannabinoid receptor agonists have become popular among those experiencing homelessness due to their low cost and high potency. Conclusions Use of synthetic cannabinoid receptor agonist MDMB-4en-PINACA is common within the male homeless population in Birmingham, United Kingdom, and remained prevalent over the 4-year study period.

This review aims to provide a thorough overview of AI's role in drug discovery . AI can aid in various stages of drug discovery in various ways, including disease identification, target acquisition, computational screening, predicting drug toxicity, gene editing for developing gene therapies, and AI-based modeling for personalized drug dosing. We examine the current state-of-the-art AI technology and its potential to revolutionize drug discovery.

Despite advances in current anti-cancer therapies, challenges such as drug resistance, toxicity, and tumor heterogeneity persist. The limitations of traditional single-target drugs and simple combination therapies are becoming increasingly apparent[1][1]. To address these issues, a novel treatment

Although 22% of new HIV diagnoses in the USA are attributed to heterosexual contact, uptake of pre-exposure prophylaxis (PrEP) remains low among heterosexually active individuals and information about PrEP considerations in this population is limited. We report on a cross-sectional qualitative study exploring attitudes toward PrEP among a diverse sample of 50 heterosexually active cisgender adults (31 women, 19 men) in New York City. Approximately, two-thirds (34) had previously heard of PrEP, but none had used it. Many learned about PrEP through marketing and social interactions, leading them to perceive it as primarily intended for gay or bisexual men and transgender individuals. Most had never discussed PrEP or received information about it in healthcare settings, despite recent HIV screenings. Participants generally viewed PrEP as irrelevant because they perceived themselves at low risk for HIV, considering it appropriate only for people with many sex partners or partners with HIV. Some said they would view a potential partner using PrEP positively (e.g., as responsible), while others expressed concerns about their presumed behaviors. Concerns about side effects and long-term drug toxicity were common and a few participants expressed anti-medication beliefs. While awareness appears high, heterosexually active New Yorkers do not seem to have started to think of PrEP as one of their HIV prevention options. PrEP promotion for this population could benefit from: messaging targeted to heterosexual adults; ensuring health-care providers inform all sexually active patients about PrEP; and clarifying for practitioners and the public that PrEP is an option for any sexually active person, regardless of self-reported HIV risk.

Candida species are notorious opportunistic pathogens that can cause severe infections, especially in immunocompromised and medically vulnerable patients. Despite the availability of various antifungal drugs, their efficacy is often limited by drug toxicity and antifungal resistance. Therefore, repurposing existing clinically approved drugs is a promising approach to addressing this challenge. In this study, we investigated the potential synergistic effects of combining the antidepressant atomoxetine (ATMX) with the antifungal agent fluconazole (FLC) against Candida albicans. Our results demonstrated that ATMX (31.25 μg/mL) and FLC (1.95 μg/mL) exhibited a trend toward synergistic activity C. albicans. We performed RNA-Seq analysis to investigate the transcriptional response of C. albicans treated with ATMX-FLC combination. Our analysis indicated that the combination has a significant impact on ribosome biogenesis, secondary metabolite biosynthesis, and metabolic pathways. Overall, our findings highlight the potential of repurposing clinically approved drugs as a novel therapeutic strategy for the treatment of Candida infections, which continue to pose a significant global public health challenge.

Autoimmune diseases (AIDs) affect millions of individuals worldwide, impacting ≈10% of the population, with women being more susceptible to them than men. AIDs include multiple sclerosis (MS), type 1 diabetes (T1D), and rheumatoid arthritis (RA), which arise when the immune system unintentionally targets healthy cells. A complex interaction among immunological, environmental, and genetic factors causes AIDs by impairing the capacity of the immune system to discriminate between self‐ and non‐self‐antigens. This impairment makes the immune system of the body attack its own tissues, causing inflammation and damage. Gut microbiota, also referred to as the “forgotten organ,” is crucial for immune system control, with dysbiosis causing or worsening AIDs. Nanotechnology provides innovative approaches to targeted medicine delivery, reduction of adverse effects, and improved treatment efficacy, providing novel avenues for treating AIDs at nanoscale. Liposomes, hydrogels, and polymeric nanoparticles are used as drug carriers to directly deliver drugs to the impacted tissues. This technology increases treatment precision, while lowering systemic toxicity, guaranteeing localized and regulated drug release—an advantage valuable for long‐term management of AIDs. In this comprehensive review, the pathophysiological connection among AIDs, role of gut microbiota, and combined nanotechnology‐based management of these diseases are addressed.

Medications, especially those taken orally, inevitably come into contact with microorganisms inhabiting the gastrointestinal tract (GIT). The intestinal microbiota can produce enzymes with various catalytic activities and, thus, affect the biological activity, bioavailability and toxicity of several natural or synthetic substances, including medicines, as well as biotransform drugs into other chemical forms or metabolites that can alter the effectiveness or toxicity of the initial active substance. The intestinal microbiota can also indirectly influence the effectiveness and toxicity of drugs by altering the body’s ability to metabolize or the processes occurring in the body (affecting liver function, altering the expression of liver enzymes or metabolic genes, interfering with detoxification processes). On the other hand, drugs like xenobiotics can have a negative effect on the microorganisms of the gastrointestinal tract, which can contribute to the development of side effects from the digestive system. The article discusses possible interactions between the intestinal microbiota and nonsteroidal anti-inflammatory drugs.

Advances in kidney organoid technologies have expanded opportunities to model human renal development, disease, and therapeutic response. Yet pluripotent stem cell-derived organoids remain limited by cellular heterogeneity, incomplete tubular maturation and low scalability, restricting their translational relevance. Tubular-specific organoids, derived from adult kidney epithelium, address many of these constraints by providing stable, reproducible cultures enriched for functional proximal and distal tubular cells. Their polarized transport, metabolic activity and patient-specific phenotypes enable high-fidelity modeling of acute and chronic tubular disorders, nephrotoxicity, and inherited tubulopathies-areas where conventional animal and cell-line models often fall short. In this Perspective, we outline recent advances that position tubuloids as a versatile platform for drug screening, toxicity testing and personalized disease modeling. We highlight emerging integration with microfluidics, biomaterials, and gene-editing strategies that promise greater physiological realism and precision therapeutics. We also discuss persistent barriers that impede broader adoption and clinical translation. We propose a roadmap for advancing tubuloid technologies toward precision nephrology and their future incorporation into diagnostic, pharmacological and regenerative pipelines.

Abstract This study aimed to evaluate the potential protective effect of silymarin (SLM) against the harmful effects of moxifloxacin (MXF) on liver tissue, utilising markers of oxidative stress, inflammation, apoptosis and endoplasmic reticulum (ER) stress. In the experimental study, rats were divided into five groups: control, SLM, MXF, MXF + SLM 25 and MXF + SLM 50. Applications were carried out for 7 days; liver tissues were examined histopathologically and the levels of molecular markers such as AST/ALT, MDA/GSH/GPx/SOD/CAT, Bax/Bcl-2/caspase-3, JAK2/STAT3, MAPK14/NF-Κb/TNF-α/IL-1β and PERK/ATF6/GRP78 were analyzed. In the MXF-administered group, notable liver tissue histopathological damage, a marked increase in oxidative stress, pro-inflammatory and pro-apoptotic markers, and a decrease in antioxidant and anti-apoptotic markers were observed. Additionally, increases in GRP78, PERK and ATF6 levels related to ER stress were observed. It was determined that these effects were significantly suppressed, liver histology was preserved, and molecular markers approached control levels in the groups in which SLM was applied together with MXF. The data collected revealed that silymarin showed a protective effect against moxifloxacin-induced liver toxicity, and this effect occurred by modulating oxidative stress, inflammation, apoptosis and ER stress responses. SLM can be considered a potential natural protective agent against drug toxicities. Graphical Abstract

Oligonucleotide therapeutics (ONTs) are a growing class of nucleic acid-based therapies with the potential to treat a variety of diseases through several different mechanisms of action (MoA). The most prevalent MoA entails the inhibition of therapeutically relevant levels of protein expression via mRNA degradation, exemplified by numerous approved ONTs. ONTs have unique physicochemical properties, pharmacokinetic characteristics, and MoAs that are distinct from other drug classes. The result can be a disconnect between the administered dose, plasma drug concentrations, pharmacodynamics, and toxicity. Thus, ONT development poses unique scientific and regulatory challenges that are not fully addressed by current International Council for Harmonisation (ICH) guidelines. The complexity of ONT development has recently been recognized with the acceptance of the new ICH topic proposal "ICH S13-Nonclinical Safety Evaluation of Oligonucleotide-based Therapeutics." In preparation for the ICH S13 Step 2 public consultation period, the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) DruSafe ONT Working Group convened to identify and discuss key considerations for ONT development, and to communicate areas where regulatory clarity and harmonization are most needed. Specifically, these include strategies to assess off-target toxicities, safety pharmacology, general toxicity studies, First-In-Human dose selection, genotoxicity studies, reproductive and developmental toxicity studies, carcinogenicity assessments, and the appropriate use of a surrogate molecule in development. The intended goals of this paper are to provide a means for communicating input towards the ICH process and to provide a resource for assisting with the public review of the draft ICH S13 Guidance.

Introduction: The integration of cell line features and drug features in computational Cancer Drug Response (CDR) prediction methods enables a nuanced understanding of cellular responses and drug effects, which may lead to improvements in drug discovery and precision oncology. It helps identify promising drug candidates for experimental validation, avoid treatments that are unlikely to benefit a patient, and reduce unnecessary exposure to toxic drugs. Methods: In this paper, we propose DCGPert-CDR, which integrates drug structural features, cell line multi-omics data, and target gene perturbation profiles for predicting IC50 responses. The methodology involves the extraction of cell line multi-omics data, including genomics, transcriptomics, and epigenomics, together with the molecular structural features of the drug. The gene perturbation profiles are computed from transcriptional changes of the prioritized target genes before and after the drug treatment. A graph clustering approach, followed by network propagation, is applied to prioritize drug target genes. The resultant feature vectors are concatenated and fed into a prediction module, consisting of a ResNet, which predicts the IC50 values of drugs across various cancer cell lines. Results: DCGPert-CDR produces promising results when compared to similar methods, with Pearson’s correlation rp of 0.841 and Spearman’s correlation rs of 0.786 computed between predicted and actual IC50 values, while for other methods, rp was in the range of 0.768 to 0.8183 and rs was between 0.735 to 0.757. Drugs such as Foretinib, Crizotinib, Tivozanib, SNX-2112, and PHA- 665752 are found to be most sensitive after analyzing the predicted response values across various cancer cell lines. Discussion: The improved results indicate that the proposed method effectively predicts responses that closely match the known IC50 values. Case studies are conducted on 24 TCGA cancer types, also revealing sensitive drugs for each cancer type, which are corroborated with clinical evidence. Dependence on the availability of drug and cell line data, as well as the absence of real-time data validation, remains a key challenge. Conclusion: The method can reliably capture the relationship between drugs and cell lines, indicating its potential utility in predicting drug sensitivity. The method effectively identified the most sensitive drugs among individual cancer types.

As research into medicinal chemical synthesis deepens, an increasing number of novel drug molecules with promising clinical therapeutic potential are emerging. During drug development, many compounds are discarded due to excessive toxic side effects, while traditional toxicity testing faces challenges of high cost and lengthy timelines. To enable rapid toxicity assessment by researchers, this paper proposes a drug toxicity prediction model (TGT) based on GNN-Transformer. The model was constructed and validated using the Tox21 dataset, with caffeine selected for practical generalisation testing. The Tox21 dataset encompasses toxicity test results for diverse compounds, providing high-quality data. The model architecture leverages graph neural networks (GNN) to process molecular graph-structured data, effectively capturing complex topological relationships and chemical information within molecules. This transforms molecular graph structures into meaningful node feature representations. The Transformer component, with its exceptional sequence modelling capabilities, further learns from GNN-extracted features, capturing long-range dependencies between molecular structures. Through training and optimisation, the model demonstrated commendable performance in toxicity prediction tasks, achieving an average AUC of 0.7488 on the validation set. Its high accuracy in predicting drug toxicity was further validated through practical application on caffeine molecules, establishing it as an efficient and precise predictive tool for early-stage drug safety assessment.The GNN-Transformer drug toxicity prediction model proposed in this study enhances prediction reliability by integrating multi-task learning with interpretability techniques. It serves as an auxiliary pre-screening tool for drug development, thereby helping to shorten the research and development cycle and reduce costs.

Liver dysfunction is a frequent yet underrecognized feature of critical illness, affecting up to 20-40% of ICU patients. Even mild abnormalities in liver function tests are associated with increased morbidity, prolonged ICU stay, and higher mortality. Hepatic dysfunction may be the primary reason for ICU admission or develop during the ICU stay. Its clinical presentation is broad, ranging from abnormal liver function tests due to sepsis, drug toxicity, or hypoxia during critical illness, to acute liver failure in previously healthy individuals, and acute-on-chronic liver failure in patients with preexisting liver disease. Recent advances highlight the importance of dynamic liver function testing, such as indocyanine green clearance, in early detection and prognostication. Moreover, evolving concepts around acute-on-chronic liver failure (ACLF) emphasize its systemic nature and the need for timely intervention, including liver support therapies and transplantation. Understanding the interplay between hepatic dysfunction and multiorgan failure is essential for improving outcomes in this vulnerable population. This review highlights recent advances in understanding into the mechanisms, diagnosis, and management of liver dysfunction in critically ill patients, with a particular emphasis on ACLF.

Levothyroxine (LT4) is the standard treatment for hypothyroidism and the most widely prescribed medication worldwide. Although generally safe, regulatory reports list potential cardiac, neuropsychiatric and musculoskeletal adverse events (AEs). Clarifying their clinical relevance is essential. We systematically searched MEDLINE, Embase, CENTRAL and Google Scholar for randomized controlled trials (RCTs) and observational studies reporting predefined AEs. Eligible comparators included placebo, no treatment, liothyronine (LT3), usual-dose LT4 or LT4 monotherapy when LT4/T3 combination therapy was studied. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated. TSH values at baseline and post-treatment were recorded to contextualize AEs by thyroid status (euthyroid, suppressed or hypothyroid). Twelve studies (n = 1817) were included: seven RCTs, two crossover trials, two case-control studies and one quasi-experimental study. Cardiac AEs (tachycardia, palpitations, angina, atrial fibrillation), neuropsychiatric AEs (headache, tremor, insomnia, anxiety, depression) and musculoskeletal AEs (myalgia) were reported; however, most comparisons were statistically nonsignificant and clinically not meaningful, indicating that LT4 at replacement doses is generally safe. AEs primarily occurred when TSH was suppressed, whereas in euthyroid patients, blinded placebo-controlled trials showed no significant differences, confirming a very low risk of LT4-related side effects. Some subjective symptoms may reflect placebo or nocebo effects rather than true drug toxicity. LT4 at replacement doses is safe, and AEs are predominantly associated with suppressed TSH or subjective effects. Interpretation of AEs should always consider thyroid status, highlighting the importance of maintaining euthyroidism.

Safer supply programs (SSPs) have emerged in Canada in a response to the drug toxicity crisis and to the contaminated illicit drug supply. A form of safer supply, injectable opioid agonist treatment (iOAT) programs, provide individuals with access to regulated opioids that are generally self-administered in supervised settings. The implementation of SSPs has been controversial, and gathering additional data on their impact is necessary to inform their future. An iOAT program in New Brunswick, Canada, was evaluated from the perspectives of individuals accessing the program, to better understand program experiences, impacts, and barriers. Semistructured interviews were completed with iOAT participants ( N = 21) who had been in the program for approximately six months. An Interpretive Phenomenological Analysis was used to understand the experiences of participants in the program. Participants reported positive experiences that were associated with the program's low-threshold and nonstigmatizing environment. Impacts such as improvements in mental health, increased financial stability, decreased engagement in criminal activities, and reduced use of substances from the illicit market were reported. Challenges associated with program involvement included inadequacies in the strength of available medications, negative impacts from interruptions to safer supply (e.g., from being arrested or hospitalized), and barriers to access (e.g., transportation and daily attendance). The findings from this study demonstrate the importance of SSPs as a vital option in the care of people who use drugs and identify the need for wraparound health services as well as increased continuity of care from SPPs to services available in healthcare and criminal justice settings.

Objective: The objective of the study is to identify uptake of adjuvant immunotherapy (AIO) and assess patient outcomes following partial or total nephrectomy for renal cell carcinoma (RCC). Patient and methods: Electronic health records of all patients who underwent partial or radical nephrectomy at a tertiary referral centre were reviewed to identify eligible RCC patients for AIO between 19/10/2022 and 19/10/2024. Hospital records of eligible patients were reviewed to obtain demographic information, details of oncological treatment, and treatment outcomes. Results: Of 143 patients who underwent operative management for suspected RCC, 40 were eligible to receive adjuvant immunotherapy. Thirty-six patients (90%) were offered referral to Oncology for adjuvant immunotherapy, of whom 34 were reviewed. Twenty-two patients (68.75%) consented to AIO. Twenty-one consented patients (95.5%) commenced AIO; one did not receive AIO due to being outside the required 12-week treatment window. Two patients (9.5%) had documented disease progression during AIO; one discontinued treatment and the other commenced Lenvatinib alongside AIO. A further nine (42.9%) patients stopped AIO due to drug toxicity. Conclusion: The majority of eligible patients were referred for AIO. Rates of progression on AIO in this ‘real-world’ setting were consistent with published clinical trial data; however, drug toxicity was higher. Pathway development is imperative to ensure that eligible patients are referred for AIO promptly due to time-critical eligibility criteria. Level of evidence: 2c

The rising prevalence of cancers with chronic inflammation is a leading global health concern. Modern anticancer treatments require dual inhibitors of cyclooxygenase- 2 (COX-2) and Epidermal Growth Factor Receptor (EGFR) with minimal adverse effects. The emergence of diaryl ether derivatives has encouraged the development of new therapeutic strategies for inflammation-linked conditions. A series of nine diaryl ether derivatives was designed, and in silico studies were performed to predict their interactions with COX- 1, COX-2, and EGFR proteins. Drug likeness and toxicity characteristics were investigated using Swiss ADME, ADMET Lab 2.0, and ProTox-3.0 tools. Furthermore, to observe the selective COX-2 inhibitory nature, a protein (bovine serum albumin (BSA)) denaturation study of the five best compounds with high binding affinities was conducted. Nine diaryl ether derivatives were synthesized and characterized using 1H-NMR, 13C NMR, LC-MS, and FT-IR spectroscopy. Based on the docking score and pharmacokinetics, compound 12a showed a significant IC50 value of 39.24 μg/mL in the BSA denaturation assay. To screen the kinetic behavior of five selected compounds with high binding affinities (11a, 12a, 12b, 12c, and 13a) with COX-2, Molecular Dynamics (MD) simulations were performed for 100 ns. MD simulations and binding free energy calculations were performed to observe the stability of the best-docked EGFR-12a complex with a docking score of -9.5 kcal/mol. This study focused on the synthesis and biological analysis with theoretical explanations. Overall, diaryl ether derivatives are promising precursors for anti-inflammatory and anticancer drugs in the biomedical field.

Cancer is the leading cause of death in most developed nations. Although significant efforts have been made to develop targeted cancer chemotherapy drugs for decades, dosing of chemotherapy drugs is still limited by systematic toxic side effects. To reduce the toxicities of chemotherapy, we have designed a 3D printed biosponge adsorber that can capture the excess untrapped chemotherapy drugs in situ before they circulate throughout the body. Specifically, we focused on liver cancer because of the liver's proximity to the heart with a model drug, doxorubicin (Dox), a highly effective chemotherapy drug with severe cardiac failure risk. Our adsorbers were prepared by forming porous lattice scaffolds via 3D printing and then adding a thin drug (Dox)-adsorbing layer of sulfonated nanostructured block copolymer on the scaffolds. The porous lattices were designed to provide a large surface area for effective drug capture but not to impair the blood flow. The drug-adsorbing block of the polymer layer is polystyrenesulfonate (PSS), which strongly binds to Dox. Using these design parameters, we have successfully placed the adsorbers in the veins downstream of the liver, i.e., the hepatic veins and inferior vena cava (IVC) draining the liver, while the drug (Dox) was injected directly to the liver, mimicking the state-of-the-art, intra-arterial chemotherapy (IAC) procedure for liver cancer patients. Our adsorbers can capture a significant amount of the excess untrapped Dox in situ. The adsorbers can significantly reduce Dox accumulation in the heart (50%) and kidneys (36%) as well as in the surrounding bloodstream (25-45%). Cell viability studies using H9c2 cells confirmed that our adsorbers reduce Dox-induced cardiotoxicity. Additionally, the placement of the adsorbers neither severely impairs the blood flow nor significantly raises blood pressure in the adjacent veins. This confirms the feasibility of the in vivo adsorption approach. Our development poses a potential new route to minimize off-target chemotherapy toxicities and thus help people fight cancer by enabling high-dose locoregional chemotherapy.