Dose Of Drug Research Articles

Performance of Creatinine and Cystatin-Based Equations on Estimating Measured GFR in People with Hematological and Solid Cancers.

Glomerular filtration rate (GFR) assessment is important in clinical practice, with implications for diagnosis, prognostication, and drug dosing. People with cancer are at risk of imprecision in GFR estimation. This cross-sectional study evaluated the performance of various creatinine and cystatin C-based equations in comparison to measured GFR (mGFR) in people with cancer. We retrieved data for all adult patients who had mGFR by urinary iothalamate clearance between 2011 and 2023 at Mayo Clinic and use of an electronic health record diagnosis code for cancer within two years prior to mGFR. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), European Kidney Function Consortium (EKFC), and Cockcroft-Gault (CG) equations were computed, along with performance metrics (bias, precision, and root mean square error, RMSE). Confidence intervals were generated by bootstrapping and analysis were stratified by solid and hematological cancer. From all adults with cancer and mGFR, 1145 had both creatinine and cystatin C available within seven days of mGFR. Among all equations, the creatinine- cystatin C CKDEPI equation provided the best performance, with small bias (median 3.0, 95%CI 2.3-3.8) and higher precision (RMSE 14.5) compared to creatinine-only or cystatin-only equations (RMSE varying from 16.6 to 20), and this was also true in solid and hematological cancers. The creatinine-cystatin EKFC equation had a similar performance to CKDEPI, CG showed worst precision (30% of people with errors above 30%), and cystatin C CKDEPI equation was the most biased, prone to underestimation of mGFR. In our cohort of patients with mGFR and cancer, the CKDEPI creatinine-cystatin C equation performed best for GFR assessment, and this was true for both solid and hematological cancers. Our findings give support for the preferential use of creatinine and cystatin C-based equations instead of creatinine-only or cystatin C-only equations in people with cancer.

Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations.

A variety of classical machine learning (ML) approaches has been developed over the past decade aiming to individualize drug dosages based on measured plasma concentrations. However, the interpretability of these models is challenging as they do not incorporate information on pharmacokinetic (PK) drug disposition. In this work we compare drug plasma concentraton predictions of well-known population PK (PopPK) modeling with classical machine learning models and a newly proposed scientific machine learning (MMPK-SciML) framework. MMPK-SciML allows to estimate PopPK parameters and their inter-individual variability (IIV) using multimodal covariate data of each patient and does not require assumptions about the underlying covariate relationships. A dataset of 541 fluorouracil (5FU) plasma concentrations as example for an intravenously administered drug and a dataset of 302 sunitinib and its active metabolite concentrations each as example for an orally administered drug were used for analysis. Whereas classical ML models were not able to describe the data sufficiently, MMPK-SciML allowed us to obtain accurate drug plasma concentration predictions for test patients. In case of 5FU, goodness-of-fit shows that the MMPK-SciML approach predicts drug plasma concentrations more accurately than PopPK models. For sunitinib, we observed slightly less accurate drug concentration predictions compared to PopPK. Overall, MMPK-SciML has shown promising results and should therefore be further investigated as a valuable alternative to classical PopPK modeling, provided there is sufficient training data.

Optimizing the Future: A Game Theory to Tumor Therapeutic Strategies

BackgroundCancer poses significant economic and societal burdens on countries in the coming decades. During chemotherapy, patients frequently encounter adverse reactions. Recent research has revealed that Chinese medicine plays a crucial role in mitigating the side effects of chemotherapy. Therefore, in this article, we propose that the cancer treatment process can be likened to an unequal game. To refine treatment strategies, we suggest employing the Steinberg model to incorporate Chinese medicine into the chemotherapy regimen for tumor treatment.ResultsWe found that when malignant tumors exhibit vigorous proliferation, doctors should administer Chinese medicine in conjunction with chemotherapy drugs, continuously optimizing the therapeutic effect of the Chinese medicine. Upon reaching a specific threshold in the treatment effect of the Chinese medicine, doctors may appropriately augment the dosage of chemotherapy drugs, building upon the initial regimen. Conversely, in cases where the proliferation ability of malignant tumors is weak, the dosage of chemotherapy and the adjuvant therapy with Chinese medicine should be kept in a relatively balanced state. Once the effect of the Chinese medicine attains a particular threshold, the dosage of chemotherapy can be concurrently increased to achieve a superior therapeutic result.ConclusionsFrom a game theory perspective, doctors can devise strategies to minimize drug toxicity and improve tumor treatment outcomes by coordinating the use of chemotherapy drugs with appropriate adjustments to Chinese medicine therapy methods.

Endocrinological toxicities related to immunotherapy combinations for advanced renal cell carcinoma: practical expert-based management recommendations.

Nowadays immune-based combinations are the standard first-line treatment for metastatic renal cell carcinoma and involve the use of either two immunotherapy agents or an immunotherapeutic drug associated with a tyrosine kinase inhibitor. Treatment-related toxicity is the primary cause of drug discontinuation or dose reduction. A thorough understanding of the prevention and management of adverse events of the immune-based combinations is critical to ensure the success of treatment. Endocrinological toxicities during treatment with immune-based combinations are frequent and often manageable. However, in some cases, diagnosis can be complex, and the treatment requires multidisciplinary discussion. In addition, it is often challenging to determine which agent in the combination is responsible for a specific toxicity. In this review, we analyze the evidence regarding treatment-related endocrinopathies in renal cell carcinoma first-line therapy. We also discuss monitoring strategies to diagnose endocrinological adverse events and provide some practical tools for their daily management.

Deferiprone ameliorates cisplatin induced peripheral neurotoxicity via ferritinophagy adjustment

Cisplatin-induced neurotoxicity is one of the limiting factors to its use especially in tumors that demand high drug dosage. One of the Cisplatin pathways is ferritinophagy which may end up in ferroptosis. So, we aimed to use iron chelator as a new strategy based on an anti-ferroptotic mechanism and to evaluate its neuroprotective effect against polyneuropathy in Cisplatin-treated rats. Twenty-four male Wistar albino rats were arranged into four groups: (I) Control group, rats were given vehicle; (II) Def group, rats received deferiprone (200 mg/kg orally once daily for 10 days); (III) Cis group, rats were injected by Cis 2 mg/Kg once daily for 3 consecutive days i.p.; and (IV) Cis + Def group, rats received deferiprone (200 mg/kg orally once daily for 10 days, rats were injected with Cis in the 4th, 5th, and 6th days). Cis increased and upregulated ferritinophagy inducers significantly including MDA, NCOA4, and IREB1 as compared to the control group. On the other hand, GSH, GPX4, SLCA11 and FTH1 were decreased and down regulated significantly compared to the control group. In addition to significant deterioration in the histopathological and immunological nerve tissue assessment using silver stain and PNCA. Embracing the cisplatin dosage with deferiprone reversed cisplatin-induced neuropathy, in which the physiological function significantly improved along with the immune and histopathology of nerve tissue. This was accompanied by down regulation of ferritinophagy inducers and enhancing ferritinophagy inhibitors. The current results concluded that rapping cisplatin with deferiprone can mitigate neurotoxicity induced by cisplatin in experimental animals through ferritinophagy pathway adjustment.

Effect of cilnidipine on depression-like behaviour in male Swiss mice: A study using the tail suspension test

Objective: The objective is to evaluate cilnidipine and compare it with fluoxetine on the depression model in male Swiss mice, utilising the tail suspension test and locomotor activity test. Materials and Methods: The animals were categorised into four groups, each consisting of six individuals (n = 6 per group). The subjects were given the test drug doses of cilnidipine at 5 mg/kg and 10 mg/kg, as well as fluoxetine at 10 mg/kg, for a duration of 21 days through an intraperitoneal route. On days 1, 14 and 21, the locomotor activity was assessed using the actophotometer, while the antidepressant activity was assessed using the tail suspension test (TST). The duration of immobility was assessed using the total sleep time method for a 5-minute period, while the number of counts was monitored for 10 min using an actophotometer. Results: Cilnidipine at a dosage of 10 mg/kg demonstrates a reduction in symptoms of depression when compared to the standard control. Neither cilnidipine 5 mg/kg nor 10 mg/kg resulted in a noteworthy decrease in locomotor activity. Conclusion: The present study demonstrated a substantial antidepressant effect of cilnidipine 10 mg/kg dosage. More research is needed to validate the results reported.

New-Onset Anti-LGI1 Encephalitis in a Pregnant Woman

Background and Clinical Significance: Anti-Leucine-rich glioma inactivated-1 (anti-LGI1) encephalitis is a rare, autoimmune disorder often presenting with limbic encephalitis. The reported incidence of anti-LGI1 is 0.83/million/year, with elderly males accounting for the overwhelming majority of cases. While anti-LGI1 encephalitis is a well-known cause of autoimmune encephalitis in men over 50, our literature review found no published cases in pregnant women. The purpose of this study is to describe a rare presentation of this pathology in an unexpected population. Case Presentation: A 21-year-old gravida 2, para 1001 woman at 20 weeks’ gestation presented with worsening seizure-like activity for the past four months, frequent falls, loss of consciousness, and concern for trauma to the abdomen. Her neurologic workup one month prior revealed a 72 h electroencephalography (EEG) with epileptic seizures of the left frontotemporal region, but a normal magnetic resonance image (MRI) of her head. A repeat MRI during this hospitalization showed bilateral limbic and basal ganglia T2 hyperintensities. She was treated with increasing doses of antiepileptic drugs without improvement and was transferred to a neurology intensive care unit, where she was diagnosed with anti-LGI1 encephalitis. She was initially treated with oral corticosteroids with inadequate response, then with intravenous immunoglobulin therapy (IVIG). Her seizure activity persisted throughout her pregnancy, requiring multiple admissions for IVIG, but she eventually delivered a healthy baby and continues to receive long-term care for her new diagnosis. Conclusions: This case illustrates classic findings of anti-LGI1 encephalitis in a non-classic patient population. Knowledge that such a case exists may serve to broaden the differential diagnoses when physicians are presented with a similar pregnant patient and expand the reported patient population in this rare disease.

Pharmacodynamic interaction profile of apigenin with diclofenac in an experimental model of inflammation in rats

Objectives: Chronic use of non-steroidal anti-inflammatory drugs(NSAID’s) is often limited by their side effect, necessitating the development of safer therapeutic alternatives. Apigenin, A plant flavonoid, has shown potential in enhancing the efficacy of NSAIDs while potentially reducing their adverse effects. This study aimed to evaluate the synergistic effects of apigenin combined with a subtherapeutic dose of diclofenac on inflammation in a rat model, to determine whether such a combination can improve anti-inflammatory effectiveness and reduce the drug dose required. Materials and Methods: A total of 42 Wistar albino rats were randomised into seven groups to receive various treatments including control (normal saline), standard diclofenac (100 mg/kg), three doses of apigenin (10, 20 and 40 mg/kg), a subtherapeutic dose of diclofenac (50 mg/kg) and a combination of apigenin (20 mg/kg) with diclofenac (50 mg/kg). Inflammation was induced using carrageenan, and paw volume was measured at multiple time points to assess oedema. The percentage change from baseline paw volume and percentage protection against paw oedema was calculated for each group. Results: The study found that the combination of 20 mg/kg apigenin with 50 mg/kg diclofenac significantly enhanced the anti-inflammatory response, achieving a peak reduction in paw volume of up to 49.66% at 3 h post-administration. This was superior to the responses seen with apigenin or diclofenac alone, where the maximal dose of apigenin (40 mg/kg) only managed a peak reduction of 28.18%, and the standard dose of diclofenac (100 mg/kg) reached a peak inhibition of 66.66% at 6 h. The subtherapeutic dose of diclofenac (50 mg/kg), used alone, showed a lower peak effect of 21.47% at 3 h. These results underscore the enhanced efficacy of the combination therapy, potentially allowing for lower doses of diclofenac while maintaining effective anti-inflammatory action. Conclusion: The findings suggest that combining apigenin with diclofenac enhances anti-inflammatory responses, supporting the potential of flavonoid-NSAID combinations as more effective and safer anti-inflammatory therapies. Future studies should explore the long-term effects and mechanisms of action to fully understand the benefits and limitations of such combinations in managing chronic inflammation.

Artificial intelligence and ageing populations: Can robotics and AI address the rising needs of the elderly?

PurposeThis opinion letter explores the role of artificial intelligence (AI) and robotics in addressing the growing needs of ageing populations, highlighting key advancements and challenges associated with their application in eldercare. By analysing recent technological developments, practical applications and ethical considerations, the paper provides a comprehensive overview of how AI and robotics could transform eldercare systems.Design/methodology/approachUsing a multidisciplinary approach, this opinion letter incorporates findings from current research on robotics and AI in care for the elderly, with an emphasis on recent technical developments as well as ethical issues.FindingsThe study shows that AI and robotics can have a massive impact in improving the quality of life of elderly persons by assisting them in health assessment, dosing of drugs, encouraging interactions and hence reducing the centralized pressure on healthcare centres. However, moral issues such as privacy, depersonalization of care and disparity in accessing such technologies in the socio-economic setup are huge barriers that have to be surmounted in order to provide optimal solutions for eldercare.Originality/valueThe novelty of this analysis is evident in its use of AI and robotics as not only potential solutions in technologies to bring better health and social communications to elderly people but also in coming up with a critical discussion of privacy and access issues, as well as several encouraging views for future eldercare.

Low-Dose Versus High-Dose Lenvatinib in Radioiodine Refractory Differentiated Thyroid Cancer-A Real-World Safety and Efficacy Analysis.

Lenvatinib, a tyrosine kinase inhibitor, is approved for the treatment of radioiodine refractory differentiated thyroid cancer (RR-DTC) at a dose of 24 mg/day. Given its significant toxicity profile, the present study aimed to compare the safety and efficacy of initial low-dose lenvatinib to that of higher starting doses in patients with RR-DTC. This retrospective study included patients with RR-DTC who were classified as: Group-A: patients receiving 10mg/day, and Group-B: patients receiving ≥ 14mg/day of lenvatinib as starting dose. Safety, radiological response (as per RECIST 1.1) and progression-free survival (PFS) outcomes were analysed and compared. A total of 105 patients with RR-DTC were included in this study (Group-A: 60, Group-B: 45). The study found that Group-B experienced significantly higher rates of drug interruptions (68.9% vs 48.3%, p = 0.035) and dose reductions (60% vs 11.7%; p < 0.001) compared to Group-A. Adverse events such as hand-foot skin reaction (77.8% vs 58.3%), diarrhea (28.9% vs 11.7%), hepatotoxicity (33.3-40% vs 11.7-18.3%), and electrolyte imbalance (15.6% vs 3.3%) were also more frequent in Group-B (p-values < 0.05). However, both groups showed similar objective response rates (47.1% vs 46.3%; p = 0.936) and comparable PFS outcomes (restricted mean survival time at 24 months: 22.8 vs 21.4 months, p = 0.128). The study suggests that starting with lower doses of lenvatinib, followed by dose escalation if tolerated, may offer a safer approach with significantly lower rates of drug interruptions and dose reductions, with comparable efficacy in RR-DTC patients. Further validation by larger prospective trials is warranted.

Novel combination therapy targeting oncogenic signaling kinase P21 activated Kinase-1 and chemotherapeutic drugs against triple negative breast cancer.

Most of the triple negative phenotype or basal-like molecular subtypes of breast cancers are associated with aggressive clinical behaviour and show poor disease prognosis. Current treatment options are constrained, emphasizing the need for novel combinatorial therapies for this particular tumor subtype. Our group has demonstrated that functionally active p21 activated kinase 1 (PAK1) exhibits significantly higher expression levels in clinical triple negative breast cancer (TNBC) samples compared to other subtypes, as well as adjacent normal tissues. Low PAK1 expression in TNBC was significantly linked to better prognosis, with improved overall survival (OS, p=0.00236) and relapse-free survival (RFS, p=0.0314), as shown by GOBO analysis. To confirm the role of PAK1 as a therapeutic target and to discover novel synergistic chemotherapy drug combinations, we conducted a drug combination screen using triple negative breast cancer cell lines and a mouse metastatic tumor cell line. We identified the combined inhibition of PAK1 inhibitor, NVS-PAK1 with doxorubicin/paclitaxel/methotrexate as a synergistic novel therapeutic approach for treating metastatic TNBC to improve overall survival. This study also indicated a reduction in the effective dosage of the chemotherapeutic drug when combined with NVS-PAK1. Our study demonstrates that combining NVS-PAK1 with each of the chemotherapeutic drugs' doxorubicin, paclitaxel, and methotrexate resulted in decreased colony formation, reduced wound healing capability, and diminished migratory and invasive potential in both TNBC cell lines and 4T1 in vitro. These findings were further validated in orthotopic mouse mammary tumors, confirming that simultaneous PAK1 inhibition alongside chemotherapy significantly enhanced anti-tumor efficacy and reduced metastasis.

Developing and validating a global trigger tool for assessing frequency, level of harm, and preventability of adverse drug events in pediatric inpatients units.

Medications are a major cause of harm to patients in hospitals, and several studies have found that they cause approximately 20% of injuries that occur in medical institutions. It was found that the rate of adverse drug events (ADEs) in pediatric hospitalizations ranges from 11 to 40 events per 100 hospitalizations and 1% of cases caused death.Objectives: This is a comparative and retrospective study. The overarching objective is to adapt the Pediatric Trigger Tool (PTT) of the 'Child Health Corporation of America' to pediatric wards in Israel, with the intention of using it to assess the rate of adverse events that occur during medication given in pediatric wards. The study characterized ADEs and examined the ability of the PTT to identify ADEs in relation to those that were voluntarily reported by the staff. This study included internal and surgical pediatric wards at an academic pediatric medical center. The PTT was validated on medical record data from 700 hospitalizations between the years 2015 - 2017. The study also determined, among other things: the stage of drug administration at which the events occurred, the percentage of all events that could have been prevented, the degrees of damage the ADE caused and more. The Positive Predictive Value (PPV) of the customized tool stands at 16.91%.The study found 108 ADEs in 78 hospitalizations. The ADE rate per 100 hospitalizations was 15.4, the ADE rate per 1,000 drug doses was 3.9, and the ADE rate per 1,000 hospitalization days was 22.8, of which 18.5% were preventable. The category of drugs that led to the highest number of ADEs was painkillers. Those ADEs led to a large number of adverse clinical effects: constipation, hypokalemia, vomiting, and rash. The most common reason for coming to the hospital was suspicion or treatment of a hematologic disease, followed by hospitalization due to a burn. The customized tool found 10.8 times more ADEs than those reported voluntarily-subjectively by the clinicalstaff. The study found that, properly adapted, the PTT tool can be used to detectADEs in internal and surgical pediatric wards.

AI‐assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis

AbstractBackgroundStandard‐of‐care for warfarin dose titration is conventionally based on physician‐guided drug dosing. This may lead to frequent deviations from target international normalized ratio (INR) due to inter‐ and intra‐patient variability and may potentially result in adverse events including recurrent thromboembolism and life‐threatening hemorrhage.ObjectivesWe aim to employ CURATE.AI, a small‐data, artificial intelligence‐derived platform that has been clinically validated in a range of indications, to optimize and guide warfarin dosing.Patients/methodsA personalized CURATE.AI response profile was generated using warfarin dose (inputs) and corresponding change in INR between two consecutive days (phenotypic outputs) and used to identify and recommend an optimal dose to achieve target treatment outcomes. CURATE.AI's predictive performance was then evaluated with a set of metrics that assessed both technical performance and clinical relevance.Results and conclusionsIn this retrospective study of 127 patients, CURATE.AI fared better in terms of Percentage Absolute Prediction Error and Percentage Prediction Error of 20% compared to other models in the literature. It also had negligible underprediction bias, potentially translating into lower bleeding risk. Modeled potential time in therapeutic range with CURATE.AI was not significantly different from physician‐guided dosing, so it is on‐par yet provides a systematic approach to warfarin dosing, easing the mental‐burden on guesswork by physicians.This study lays the groundwork for the prospective study of CURATE.AI as a clinical decision support system. CURATE.AI may facilitate the effective use of affordable warfarin with a well‐established safety profile, without the need for costly, new oral anticoagulants. This can have significant impact both on the individual and public health.

Impact of Critical Process Parameters on the Dimensional, Mean Weight and Swelling Properties of 3D-Printed Intravaginal Rings: A Quality by Design approach

ABSTRACT 3D printing has been emerging as a transformative technology in pharmaceutical manufacturing, offering potential for personalized medicine and innovative dosage forms. It enables precise control over drug release profiles and dosage customization, addressing individual patient needs. Various 3D printing techniques, including fused deposition modeling (FDM), are being explored for pharmaceutical applications. The choice of polymers and their rheological properties are crucial for successful extrusion-based 3D printing of pharmaceuticals. While 3D printing shows promise in accelerating drug development and facilitating large-scale manufacturing, challenges remain in terms of quality and quality control. Quality-by-design (QbD) approaches for developing 3D-printed dosage forms are essential for assessing drug content, release profiles, and overall quality to ensure safe and effective pharmaceutical products. In this study we highlight the role of critical process parameters (CPPs), such as infill density and printing speed in the production of poly (lactic acid) based intravaginal rings. The effect of the CPPs on critical quality attributes (CQAs), i.e. ring dimensions, mean weight and swelling degree is studied. The study evaluated the influence of printing speed (25-100 mm/s) and infill density (0-20%) on the weight and dimensions of 3D-printed rings using ANOVA. The results showed that printing settings significantly impacted both weight and dimensions. Average weights ranged from 0.537 g to 0.629 g, with the highest weights found in samples with the highest infill density and lowest printing speed. Internal ring dimensions varied between 9.73 mm and 9.81 mm, while external dimensions ranged from 19.43 mm to 19.69 mm. In swelling tests, rings with the lowest printing speed and highest infill density showed the greatest swelling, up to 2.47%, after 3 hours in distilled water with a pH of 4.3. Infill density and printing speed produced a pivotal impact on dimensions, weight and swelling behavior of intravaginal rings. The results demonstrate that FDM offers a viable approach to producing cost-effective, patient-specific intravaginal rings, with statistical analysis confirming the reproducibility and effectiveness of the fabricated devices.

Highly Sensitive and Interference-Free Detection of Multiple Drug Molecules in Serum Using Dual-Modified SERS Substrates Combined with AI Algorithm Analysis.

Surface-enhanced Raman spectroscopy (SERS) technology has shown broad potential in drug concentration detection, but its application in blood drug monitoring faces significant challenges. The primary difficulty lies in overcoming the interference caused by various biomolecules present in serum, which can severely obscure the SERS signals of target drug molecules. Traditional enhancement substrates are often limited to detecting single drugs and are prone to interference, making the label-free detection of multiple drugs particularly challenging. To address these issues, we developed a novel SERS substrate based on Au@AgNRs, which undergoes a two-step modification to produce Au@AgDBCNRs. This innovative substrate provides exceptional signal amplification, simultaneously allowing the sensitive detection of multiple drug molecules. Moreover, our method eliminates the need for serum deproteinization, enabling the direct detection of drugs in serum while effectively mitigating interference from blood components. The cetyltrimethylammonium bromide coating on Au@AgDBCNRs is an internal standard for drug quantification without additional standards. The platform significantly improves detection accuracy and efficiency by automatically integrating artificial intelligence to recognize and analyze Raman spectral features. This novel SERS platform provides a new idea for therapeutic drug monitoring and is expected to provide rapid, accurate, and cost-effective drug detection in the clinical environment, which has great potential in improving patient care and optimizing drug dosage strategies.

A Study Of the effect of Sex on drug dosing, concentrations, and pharmacogenomics in the Montreal Heart Institute Hospital Cohort (SOS-PGx): methodology and research progress.

Women are underrepresented in drug development trials and there is no sex-tailored drug regimen for most medications. It has been repeatedly shown that women have more adverse drug reactions than men for several medications. These differences could be explained by higher dose-adjusted drug concentrations in women. Thus, we aim to identify sex-related differences and to characterize the clinical and genetic predictors of these differences in drug concentrations, dosing, and adherence for 47 commonly used drugs in a large cohort. The objective of this article is to present an overview of the methods and characteristics of the study population. We performed a cross-sectional study that included 10,082 men and women of European ancestry aged ≥ 18years from the Montreal Heart Institute Hospital Cohort taking at least one of the 47 medications regularly. Of the 10,082 participants included, 36% were women. Women had lower weight, height, waist girth, and body mass index than men, but they had higher hip girth (all p < 0.001). Men had a higher level of education and annual income and were more likely to be employed full-time compared to women. Furthermore, men had a higher prevalence of hypertension, type2 diabetes, dyslipidemia, andmyocardial infarction (all p < 0.001) and were more likely receiving lipid-lowering agents, beta-blockers, antidiabetic drugs, and angiotensin-converting enzyme inhibitors. Conversely, proton pump inhibitors were more prevalent in women. Interestingly, nearly half of the women had a history of drug allergy or intolerance, compared with less than one-third of the men (p < 0.001). This study has a high potential in understanding eventual sex differences in drug dosing requirements and will most likely provide useful information to personalize drug regimens in women.

Kinetic estimated glomerular filtration rate and drug dosing in critically ill patients with acute kidney injury-A prospective observational study.

To study the impact of kinetic glomerular filtration rate (kGFR) on clinical decision making and its implications on drug dosing compared to that of estimated GFR (eGFR) using chronic kidney disease epidemiology collaboration (CKD-EPI) equation in critically ill patients with acute kidney injury (AKI) admitted in a tertiary level intensive care unit (ICU). Cross-sectional, prospective, observational study design. All patients admitted to Medical ICU, Fortis Hospital, Bangalore with AKI defined as per AKI network (AKIN) criteria. Patients were recruited after approval from the scientific and institutional ethics committee, with written informed consent. Serum creatinine values at admission and further values were noted. GFR was calculated using both formulas (CKD-EPI and kGFR) and documented at all intervals of creatinine sampling. Drugs requiring renal dose modification along with the dosing were documented. Sample size was calculated after a pilot study and a total of 107 patients were analyzed. Incidence of AKI was 12.84%. The mean (±SD) eGFR was 37.25 (±29.4) and kGFR was 42.5 (±33.2), (p-value .003). 70 (65.42%) patients required drug dose change when kGFR was used. Dosing changes from Day 1 to Day 5 are 53/104 (50.9%), 39/81 (48.1%), 12/26 (46.1%), 2/9 (28.5%), 1/2 (50%). Predominant dose changes were for antimicrobials: vancomycin (35.7%), acyclovir (23.1%), and meropenem (23%). Drug dosing using different methods of GFR calculation showed a difference in the dosing in 65.42% of patients with AKI. Accounting for change in creatinine over time using kinetic GFR may lead to better drug dosing in critically ill patients with AKI. Our study shows that calculating GFR using kGFR formula instead of CKD-EPI may change drug dosages among patients with AKI admitted in ICU. By replacing conventional GFR estimation formulas with kGFR we may reduce the drug dosing inaccuracies that are currently prevalent in this cohort of patients.

High drug-loaded amorphous solid dispersions of a poor glass forming drug: The impact of polymer type and cooling rate on amorphous drug behaviour.

Enhancing the aqueous solubility via amorphization of crystalline poor glass-forming drugs represents a challenge, particularly when drug dosing is high. In such scenarios, there is often a need for high polymer loadings, leading to an increase in the dosage form mass and less patient acceptability. This work investigated the role that polymer type and after-melt cooling rate had upon the amorphicity of solid dispersions (SDs) containing high levels of naproxen and three commonly used polymeric excipients: Eudragit® EPO, Kollidon® VA64, and Soluplus®. Using a combination of thermogravimetry, conventional and fast-scan DSC, oscillatory rheology, in silico Hansen solubility parameter computation, FTIR, and PXRD, we have shown that amorphicity could be affected by the cooling rate with the specific polymer type and amount playing a significant role in the degree of this impact. The amorphous drug content, evident at higher cooling rates, was found to be dependent on drug-polymer interaction and polymer melt viscosity. Higher polymer concentration and faster cooling produced less melt crystallization upon cooling, which was attributed to a shift in nucleation to lower temperatures where it could be inhibited by polymer matrix viscosity. Amorphous drug content, which contained drug nuclei, was evidenced by cold crystallization upon reheating. After 4weeks of 'gentle' storage, cold crystallization increased if nucleation was the dominant process, whereas cold crystallization decreased if crystal growth prevailed. Storage at elevated temperature and humidity resulted in the absence of cold crystallization, and increased melt crystallisation. Thus, faster cooling could serve as an additional tool to improve amorphous yield and stability of high drug-loaded SDs, however, intermolecular polymer-drug interaction, melt viscosity of the drug-polymer matrix, and storage conditions are of critical importance to achieve this goal.

Simulations probe the role of space in the interplay between drug-sensitive and drug-resistant cancer cells.

The interplay between drug-sensitive and drug-resistant cancer cells has been observed to impact cell-to-cell interactions in experimental settings. However, the role that space plays in these interactions remains unclear. In this study, we develop mathematical models to investigate how spatial factors affect cell-to-cell competition between drug-sensitive and drug-resistant cancer cells in silico. We develop two baseline models to study cells from the epithelial FaDu cell line subjected to two drugs, specifically the ATR inhibitor ceralasertib and the PARP inhibitor olaparib, that target DNA damage response pathways. Our baseline models are: (1) a temporally resolved ordinary differential equation (ODE) model, and (2) a spatio-temporally resolved agent-based model (ABM). The models simulate cells in well-mixed and spatially structured cell systems, respectively. The ODE model is calibrated against in vitro data and is thereafter mapped onto the baseline ABM which, in turn, is extended to enable a simulation-based investigation on how spatial factors impact cell-to-cell competition. Simulation results from the extended ABMs demonstrate that the in silico treatment responses are simultaneously affected by: (i) the initial spatial cell configurations, (ii) the initial fraction of drug-resistant cells, (iii) the drugs to which cells express resistance, (iv) drug combinations, (v) drug doses, and (vi) the doubling time of drug-resistant cells compared to the doubling time of drug-sensitive cells. These results reveal that spatial structures of the simulated cancer cells affect both cell-to-cell interactions, and the impact that these interactions have on the ensuing population dynamics. This leads us to suggest that the role that space plays in cell-to-cell interactions should be further investigated and quantified in experimental settings.

Global transcriptome profiling of ST09 treated breast cancer cells identifies miR-197-5p/GPX3 antioxidant axis as a regulator of tumorigenesis.

ROS (Reactive Oxygen Species) has a dual role in tumorigenesis. Some cancers have high ROS conditions, and others have low ROS. TNBC thrives on high ROS compared to other Breast Cancer subtypes. Several antioxidant enzymes catalyze the detoxification of reactive oxygen species and prevent free radicals from damaging DNA and accumulation of mutation. Curcumin, a polyphenol dietary supplement, acts as a potent antioxidant, is known to reduce inflammation, and has anticancer properties. Here, we aim to understand alterations in the transcriptome (miRNA and mRNA expression) induced by ST09 in breast cancer cell lines. We identified an antioxidant system that is upregulated in breast cancer cell lines. Among the antioxidant enzymes regulated by miRNA was GPX3. A novel miRNA-mRNA antioxidant axis, miR-197-5p/GPX3, was observed in the TNBC cell line. We further validated the regulation of GPX3 by miRNA using luciferase assay. GPX3 overexpression, knockdown, and activity assay indicated the anti-tumorigenic role of GPX3 in the TNBC cell line. Further, treatment of TNBC xenograft with ST09 showed tumor reduction in vivo. ST09 potentiates the effect of standard-of-care (SOC) drug Cisplatin in vivo. ST09 can be exploited as a single chemotherapeutic agent or in combination treatment modalities, reducing the dosage of potent drugs.