Hypoxia in uterine fibroids: role in pathobiology and therapeutic opportunities.

Uterine fibroids are the most common tumors in females affecting up to 70% of women world-wide, yet targeted therapeutic options are limited. Oxidative stress has recently surfaced as a key driver of fibroid pathogenesis and provides insights into hypoxia-induced cell transformation, extracellular matrix pathophysiology, hypoxic cell signaling cascades, and uterine biology. Hypoxia drives fibroid tumorigenesis through (1) promoting myometrial stem cell proliferation, (2) causing DNA damage propelling transformation of stem cells to tumor initiating cells, and (3) driving excess extracellular matrix (ECM) production. Common fibroid-associated DNA mutations include MED12 mutations, HMGA2 overexpression, and Fumarate hydratase loss of function. Evidence suggests an interaction between hypoxia signaling and these mutations. Fibroid development and growth are promoted by hypoxia-triggered cell signaling via various pathways including HIF-1, TGFß, and Wnt/ß-catenin. Fibroid-associated hypoxia persists due to antioxidant imbalance, ECM accumulation, and growth beyond adequate vascular supply. Current clinically available fibroid treatments do not take advantage of hypoxia-targeting therapies. Growing pre-clinical and clinical studies identify ROS inhibitors, anti-HIF-1 agents, Wnt/ß-catenin inhibition, and TGFß cascade inhibitors as agents that may reduce fibroid development and growth through targeting hypoxia.

A physiologically-based pharmacokinetic precision dosing approach to manage dasatinib drug-drug interactions.

Dasatinib, a second-generation tyrosine kinase inhibitor, is approved for treating chronic myeloid and acute lymphoblastic leukemia. As a sensitive cytochrome P450 (CYP) 3A4 substrate and weak base with strong pH-sensitive solubility, dasatinib is susceptible to enzyme-mediated drug-drug interactions (DDIs) with CYP3A4 perpetrators and pH-dependent DDIs with acid-reducing agents. This work aimed to develop a whole-body physiologically-based pharmacokinetic (PBPK) model of dasatinib to describe and predict enzyme-mediated and pH-dependent DDIs, to evaluate the impact of strong and moderate CYP3A4 inhibitors and inducers on dasatinib exposure and to support optimized dasatinib dosing. Overall, 63 plasma profiles from perorally administered dasatinib in healthy volunteers and cancer patients were used for model development. The model accurately described and predicted plasma profiles with geometric mean fold errors (GMFEs) for area under the concentration-time curve from the first to the last timepoint of measurement (AUClast) and maximum plasma concentration (Cmax) of 1.27 and 1.29, respectively. Regarding the DDI studies used for model development, all (8/8) predicted AUClast and Cmax ratios were within twofold of observed ratios. Application of the PBPK model for dose adaptations within various DDIs revealed dasatinib dose reductions of 50%-80% for strong and 0%-70% for moderate CYP3A4 inhibitors and a 2.3-3.1-fold increase of the daily dasatinib dose for CYP3A4 inducers to match the exposure of dasatinib administered alone. The developed model can be further employed to personalize dasatinib therapy, thereby help coping with clinical challenges resulting from DDIs and patient-related factors, such as elevated gastric pH.

Physiologically based pharmacokinetic modeling of imatinib and N-desmethyl imatinib for drug-drug interaction predictions.

The first-generation tyrosine kinase inhibitor imatinib has revolutionized the development of targeted cancer therapy and remains among the frontline treatments, for example, against chronic myeloid leukemia. As a substrate of cytochrome P450 (CYP) 2C8, CYP3A4, and various transporters, imatinib is highly susceptible to drug-drug interactions (DDIs) when co-administered with corresponding perpetrator drugs. Additionally, imatinib and its main metabolite N-desmethyl imatinib (NDMI) act as inhibitors of CYP2C8, CYP2D6, and CYP3A4 affecting their own metabolism as well as the exposure of co-medications. This work presents the development of a parent-metabolite whole-body physiologically based pharmacokinetic (PBPK) model for imatinib and NDMI used for the investigation and prediction of different DDI scenarios centered around imatinib as both a victim and perpetrator drug. Model development was performed in PK-Sim® using a total of 60 plasma concentration-time profiles of imatinib and NDMI in healthy subjects and cancer patients. Metabolism of both compounds was integrated via CYP2C8 and CYP3A4, with imatinib additionally transported via P-glycoprotein. The subsequently developed DDI network demonstrated good predictive performance. DDIs involving imatinib and NDMI were simulated with perpetrator drugs rifampicin, ketoconazole, and gemfibrozil as well as victim drugs simvastatin and metoprolol. Overall, 12/12 predicted DDI area under the curve determined between first and last plasma concentration measurements (AUClast) ratios and 12/12 predicted DDI maximum plasma concentration (Cmax) ratios were within twofold of the respective observed ratios. Potential applications of the final model include model-informed drug development or the support of model-informed precision dosing.

Physiologically Based Pharmacokinetic Modeling of Bergamottin and 6,7-Dihydroxybergamottin to Describe CYP3A4 Mediated Grapefruit-Drug Interactions.

Grapefruit is a moderate to strong inactivator of CYP3A4, which metabolizes up to 50% of marketed drugs. The inhibitory effect is mainly attributed to furanocoumarins present in the fruit, irreversibly inhibiting preferably intestinal CYP3A4 as suicide inhibitors. Effects on CYP3A4 victim drugs can still be measured up to 24 hours after grapefruit juice (GFJ) consumption. The current study aimed to establish a physiologically-based pharmacokinetic (PBPK) grapefruit-drug interaction model by modeling the relevant CYP3A4 inhibiting ingredients of the fruit to simulate and predict the effect of GFJ consumption on plasma concentration-time profiles of various CYP3A4 victim drugs. The grapefruit model was developed in PK-Sim and coupled with previously developed PBPK models of CYP3A4 substrates that were publicly available and already evaluated for CYP3A4-mediated drug-drug interactions. Overall, 43 clinical studies were used for model development. Models of bergamottin (BGT) and 6,7-dihydroxybergamottin (DHB) as relevant active ingredients in GFJ were established. Both models include: (i) CYP3A4 inactivation informed by in vitro parameters, (ii) a CYP3A4 mediated clearance estimated during model development, as well as (iii) passive glomerular filtration. The final model successfully describes interactions of GFJ ingredients with 10 different CYP3A4 victim drugs, simulating the effect of the CYP3A4 inactivation on the victims' pharmacokinetics as well as their main metabolites. Furthermore, the model sufficiently captures the time-dependent effect of CYP3A4 inactivation as well as the effect of grapefruit ingestion on intestinal and hepatic CYP3A4 concentrations.

Cost-Conscious Colposcopy: A Single-Institution Review of Biopsy Submission Practices and Outcomes.

OBJECTIVE: Distribution of cervical dysplasia may influence approach for excisional procedures. Separating colposcopy biopsies into multiple specimen cups for pathologic evaluation incurs additional costs. The authors aimed to determine whether the practice of separating biopsy specimens impacts patient outcomes. METHODS: A retrospective review of all colposcopy cases from a single institution was performed. A total of 1,331 cases were reviewed from January 1, 2017, to December 31, 2019. Multibiopsy cohorts were separated by number of specimen cups received by pathology (single or multiple). Cohorts were compared for histology, need for excisional procedure, and final excisional pathology results. Specimen processing fees were acquired from the Department of Pathology ($70/specimen). Statistical analysis performed on MINITAB using Pearson chi-square and Fisher exact tests. RESULTS: Excisional procedures were required by 30.4% (86/283) of multiple specimen submissions compared with 28.2% (154/547) of single specimen cup submissions ( p = .50). There was a higher, although not statistically significant, rate of additional procedures in the multiple specimen cup cohort (8.8 vs 2.9% [ p = .08]). Malignancy diagnosis was equivalent in each cohort. Cost analysis revealed adopting a single specimen cup model would reduce costs up to approximately $30,000/year. CONCLUSIONS: Patient outcomes were not improved by the practice of submitting multiple specimen cups. Given the additional cost associated with separating specimens, the authors recommend during routine colposcopy that all cervical biopsies be sent for evaluation as a single pathology specimen unless a lesion of concern is identified in an area not normally excised during traditional excisional procedures.

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators.

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim® and MoBi® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUClast and DFI Cmax ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUClast and 21/21 DDI Cmax ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUClast and 18/21 DDI Cmax ratios were within the success limits.

A Physiologically Based Pharmacokinetic and Pharmacodynamic Model of the CYP3A4 Substrate Felodipine for Drug-Drug Interaction Modeling.

The antihypertensive felodipine is a calcium channel blocker of the dihydropyridine type, and its pharmacodynamic effect directly correlates with its plasma concentration. As a sensitive substrate of cytochrome P450 (CYP) 3A4 with high first-pass metabolism, felodipine shows low oral bioavailability and is susceptible to drug-drug interactions (DDIs) with CYP3A4 perpetrators. This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) parent-metabolite model of felodipine and its metabolite dehydrofelodipine for DDI predictions. The model was developed in PK-Sim® and MoBi® using 49 clinical studies (94 plasma concentration-time profiles in total) that investigated different doses (1-40 mg) of the intravenous and oral administration of felodipine. The final model describes the metabolism of felodipine to dehydrofelodipine by CYP3A4, sufficiently capturing the first-pass metabolism and the subsequent metabolism of dehydrofelodipine by CYP3A4. Diastolic blood pressure and heart rate PD models were included, using an Emax function to describe the felodipine concentration-effect relationship. The model was tested in DDI predictions with itraconazole, erythromycin, carbamazepine, and phenytoin as CYP3A4 perpetrators, with all predicted DDI AUClast and Cmax ratios within two-fold of the observed values. The model will be freely available in the Open Systems Pharmacology model repository and can be applied in DDI predictions as a CYP3A4 victim drug.

Prolonged Opioid Use Among Opioid-Naive Women Undergoing Breast Reconstructive Surgery.

Background Patients that undergo mastectomy for breast cancer with reconstruction may be prone to prolonged opioid use. As risk factors are not well-established, this article sought to better understand the risk factors that may be associated with this. Methods Patients that underwent breast reconstruction between 2010 and 2018 were identified in PearlDiver, a national insurance claims database. Patient demographics and comorbidities were elucidated, and various complications were then identified. Descriptive statistics as well as a multivariate analysis was used to evaluate the association of risk factors and complications. Results Breast reconstruction patients of 24,765 were identified from this database. Obesity, tobacco use, benzodiazepine use, and anticonvulsant use were all associated with prolonged opioid prescriptions greater than 90 days after both alloplastic and autologous reconstruction. Conclusion Prolonged opioid use continues to remain a topic of concern, and particularly in cancer patients that undergo breast reconstruction. Providers should be aware of potential risk factors for this to reduce this chance following breast reconstruction surgery.

Assessment of glomerular filtration rate by formulas in obese patients: What is the best choice?

BACKGROUND: The equations routinely used to calculate the glomerular filtration rate (GFR) have not been validated in obese patients. MATERIALS AND METHODS: This cross-sectional study evaluated 7 formulas used to estimate GFR, analyzing the effect of using ideal body weight (IBW) through the formula IBW = desirable BMI × (height)2, using the calculated body surface area (BSAcalc) of each patient through the Dubois and Dubois formula. Bias, accuracy, and receiver operating characteristic curve were calculated. The criterion standard was the direct measurement of GFR by 24-hour urine creatinine clearance. RESULTS: Forty-five obese patients were evaluated (grade I obesity 48.89%). The formula that showed greater accuracy in grade I obesity was SalazarIBW (% error = 2.30 ± 33.92), followed by Cockcroft-GaultIBW (% error = -2.84 ± 32.76). In patients with grade II and III obesity, the most accurate formula was Chronic Kidney Disease Epidemiology (CKD-EPI) (% error = 3.84 ± 41.79), followed by the Modification of Diet in Renal Disease (MDRD) formula (% error = 4 ± 38.43). When using IBW in the Cockcroft-Gault, Sobh, and Salazar formulas, the mean GFR was closer to the criterion standard and showed an increase in the Pearson correlation. Of these 3, the one with the best performance in grade II and III obesity was Cockcroft-GaultIBW (% error = 8.90 ± 42.96). CONCLUSION: The results demonstrate that in this sample, the use of IBW improved Cockcroft-Gault performance. Cockcroft-Gault BSAcalc showed lower performance when compared to Cockcroft-GaultIBW. In grade II and III obesity, CKD-EPI and MDRD showed the best accuracy.

Novel models for the prediction of drug-gene interactions.

INTRODUCTION: Adverse drug reactions (ADRs) are among the leading causes of death, and frequently associated with drug-gene interactions (DGIs). In addition to pharmacogenomic programs for implementation of genetic preemptive testing into clinical practice, mathematical modeling can help to understand, quantify and predict the effects of DGIs in vivo. Moreover, modeling can contribute to optimize prospective clinical drug trial activities and to reduce DGI-related ADRs. AREAS COVERED: Approaches and challenges of mechanistical DGI implementation and model parameterization are discussed for population pharmacokinetic and physiologically based pharmacokinetic models. The broad spectrum of published DGI models and their applications is presented, focusing on the investigation of DGI effects on pharmacology and model-based dose adaptations. EXPERT OPINION: Mathematical modeling provides an opportunity to investigate complex DGI scenarios and can facilitate the development process of safe and efficient personalized dosing regimens. However, reliable DGI model input data from in vivo and in vitro measurements are crucial. For this, collaboration among pharmacometricians, laboratory scientists and clinicians is important to provide homogeneous datasets and unambiguous model parameters. For a broad adaptation of validated DGI models in clinical practice, interdisciplinary cooperation should be promoted and qualification toolchains must be established.

The Influence of Audible Alarm Loudness and Type on Clinical Multitasking.

In high-consequence industries such as health care, auditory alarms are an important aspect of an informatics system that monitors patients and alerts providers attending to multiple concurrent tasks. Alarms levels are unnecessarily high and alarm signals are uninformative. In a laboratory-based task setting, we studied 25 anesthesiology residents' responses to auditory alarms in a multitasking paradigm comprised of three tasks: patient monitoring, speech perception/intelligibility, and visual vigilance. These tasks were in the presence of background noise plus/minus music, which served as an attention-diverting stimulus. Alarms signified clinical decompensation and were either conventional alarms or a novel informative auditory icon alarm. Both alarms were presented at four different levels. Task performance (accuracy and response times) were analyzed using logistic and linear mixed-effects regression. Salient findings were 1), the icon alarm had similar performance to the conventional alarm at a +2 dB signal-to-noise-ratio (SNR) (accuracy: OR 1.21 (95% CI 0.88, 1.67), response time: 0.04 s at 2 dB (95% CI: -0.16, 0.24), which is a much lower level than current clinical environments; 2) the icon alarm was associated with 27% greater odds (95% CI: 18%, 37%) of correctly addressing the vigilance task, regardless of alarm SNR, suggesting crossmodal/multisensory multitasking benefits; and 3) compared to the conventional alarm, the icon alarm was associated with an absolute improvement in speech perception of 4% in the presence of an attention-diverting auditory stimulus (p = 0.031). These findings suggest that auditory icons can provide multitasking benefits in cognitively demanding clinical environments.

Physiologically Based Pharmacokinetic Modeling of Bupropion and Its Metabolites in a CYP2B6 Drug-Drug-Gene Interaction Network.

The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug-drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug-gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11ß-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug-Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach.

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent-metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug-drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration-time profiles (dosing range 50-800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

Effective Removal of Dabigatran by Idarucizumab or Hemodialysis: A Physiologically Based Pharmacokinetic Modeling Analysis.

BACKGROUND: Application of idarucizumab and hemodialysis are options to reverse the action of the oral anticoagulant dabigatran in emergency situations. OBJECTIVES: The objectives of this study were to build and evaluate a mechanistic, whole-body physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of idarucizumab, including its effects on dabigatran plasma concentrations and blood coagulation, in healthy and renally impaired individuals, and to include the effect of hemodialysis on dabigatran exposure. METHODS: The idarucizumab model was built with the software packages PK-Sim® and MoBi® and evaluated using the full range of available clinical data. The default kidney structure in MoBi® was extended to mechanistically describe the renal reabsorption of idarucizumab and to correctly reproduce the reported fractions excreted into urine. To model the PD effects of idarucizumab on dabigatran plasma concentrations, and consequently also on blood coagulation, idarucizumab-dabigatran binding was implemented and a previously established PBPK model of dabigatran was expanded to a PBPK/PD model. The effect of hemodialysis on dabigatran was implemented by the addition of an extracorporeal dialyzer compartment with a clearance process governed by dialysate and blood flow rates. RESULTS: The established idarucizumab-dabigatran-hemodialysis PBPK/PD model shows a good descriptive and predictive performance. To capture the clinical data of patients with renal impairment, both glomerular filtration and tubular reabsorption were modeled as functions of the individual creatinine clearance. CONCLUSIONS: A comprehensive and mechanistic PBPK/PD model to study dabigatran reversal has been established, which includes whole-body PBPK modeling of idarucizumab, the idarucizumab-dabigatran interaction, dabigatran hemodialysis, the pharmacodynamic effect of dabigatran on blood coagulation, and the impact of renal function in these different scenarios. The model was applied to explore different reversal scenarios for dabigatran therapy.