Oral gonadotropin-releasing hormone (GnRH) antagonists are promising agents in the treatment of endometriosis-related pain. Here we assessed the safety, pharmacokinetics (PK), and pharmacodynamics (PD) of SHR7280, an oral non-peptide GnRH antagonist in premenopausal women with endometriosis. In thePhase 1 part ofthe randomized, double-blinded, placebo-controlled, dose-ascending, Phase 1/2 trial, premenopausal women with endometriosis were randomized (4:1) to receive SHR7280 or placebo treatment for 21 consecutive days. The treatment dose started from 200 mg QD, and then increased to 300 mg QD and 200 mg BID. Safety, PK, and PD parameters were assessed. In total, 30 patients received assigned treatment, 24 with SHR7280 and 6 with placebo. SHR7280 was well tolerated. Adverse events (AEs) were reported in 19 (79.2%, 19/24) patients in the SHR7280 group and 5 (83.3%, 5/6) patients in the placebo group. Most AEs were mild and no severe AEs occurred. SHR7280 showed a rapid absorption, with a time to maximum plasma concentration (Tmax) of 1.0 h, 1.0 h, and 0.8 h for the 200 mg QD, 300 mg QD, and 200 mg BID regimens, respectively. Plasma concentration of SHR7280 was dose dependent. The mean half-life (t1/2) at steady state was 6.9 h, 7.4 h, and 2.8 h, respectively, and little or no accumulation was observed. Pharmacodynamic analysis showed that SHR7280 could effectively suppress estradiol and luteinizing hormone concentrations and prevent progesterone increase in a dose-dependent manner. SHR7280 at doses of 300 mg QD and 200 mg BID could suppress estradiol levels within the desired therapeutic window of 20-50 pg/mL throughout the treatment period. SHR7280 showed favorable safety, PK, and PD profiles in the doses of 200 mg QD, 300 mg QD, and 200 mg BID. The results of this study provide evidence to support the further development of SHR7280 as a GnRH antagonist for the treatment of endometriosis-related pain in the subsequent Phase 2 trial. Trial registration number: Clinicaltrials.gov, identifier: NCT04417972. Trial registration date: 5 June 2020.

Low molecular weight heparins (LMWH) and anti-Xa direct oral anti-coagulants (DOACs) are recommended for the long-term treatment of cancer-associated thrombosis (CAT) based on well-documented randomised controlled trials. Anti-Xa DOACs are viewed as a first choice for the treatment of patients with CAT. A large number of drug-drug interactions have been reported between DOACs and chemotherapy drugs, modifying circulating levels of DOAC leading to fears of increased bleeding risks or thrombotic recurrence. Progresses in anti-neoplastic therapies have improved the prognosis and the survival, thus increasing the prevalence of frail patients with cancer. However, since frailties tend to be excluded from large trials due to multiple co-morbidities, current guidelines are not fully applicable to this population. The management of these frail patients with CAT is particularly complex and requires a risk assessment on a case-by-case basis with specific focus on cancer, patient-related risk factors and drug-drug interactions. In this brief review we have identified age, co-morbidities and co-medications as key factors of frailty that require careful attention and we have developed a therapeutic decision algorithm to help clinicians optimising the use of anti-coagulants in patients with cancer with CAT, especially in case of anti-Xa DOACs concomitant medications. With the evaluation of the bleeding risk according to the type of cancer, and anticipating drug-drug interactions intensity, taking into account patient frailties allows the optimisation of the anti-coagulant choice. A systematic collaboration between oncologists, vascular pathology specialists and pharmacists is warranted to ensure an optimal patient management. Clinical studies are needed to determine the real impact of these interactions.

Small-interfering ribonucleic acids (siRNAs) with N-acetylgalactosamine (GalNAc) conjugation for improved liver uptake represent an emerging class of drugs that modulate liver-expressed therapeutic targets. The pharmacokinetics of GalNAc-siRNAs are characterized by a rapid distribution from plasma to tissue (hours) and a long terminal plasma half-life, analyzed in the form of the antisense strand, driven by redistribution from tissue (weeks). Understanding how clinical pharmacokinetics relate to the dose and type of siRNA chemical stabilizing method used is critical, e.g., to design studies, to investigate safety windows, and to predict the pharmacokinetics of new preclinical assets. To this end, we collected and analyzed pharmacokinetic data from the literature regarding nine GalNAc-siRNAs. Based on this analysis, we showed that the clinical plasma pharmacokinetics ofGalNAc-siRNAs areapproximately dose proportional and similar between chemical stabilizing methods. This holds for both the area under the concentration-time curve (AUC) and the maximum plasma concentration (Cmax). Corresponding rat and monkey pharmacokinetic data for a subset of the nine GalNAc-siRNAs show dose-proportional Cmax, supra-dose-proportional AUC, and similar pharmacokinetics between chemical stabilizing methods​. Together, the animal and human pharmacokinetic data indicate that plasma clearance divided by bioavailability follows allometric principles and scales between species with an exponent of 0.75. Finally, the clinical plasma concentration-time profiles can be empirically described by standard one-compartment kinetics with first-order absorption up to 24h after subcutaneous dosing, and by three-compartment kinetics with first-order absorption in general. To describe the system more mechanistically, we report a corrected and unambiguously defined version of a previously published physiologically based pharmacokinetic model.

Isavuconazole exposure-response relationships have been studied with a focus on total rather than unbound exposure, assuming a constant unbound fraction of 1%. We observed a median (range) unbound fraction of 1.59% (0.42-5.30%) in patients. This highly variable protein binding asks for re-evaluation of current pharmacokinetic and pharmacodynamic targets for isavuconazole.

Isavuconazole is a broad-spectrum antifungal agent for the management of invasive fungal disease. Optimised drug exposure is critical for patient outcomes, specifically in the critically ill population. Solid information on isavuconazole pharmacokinetics including protein binding in patients in the intensive care unit is scarce. We aimed to describe the total and unbound isavuconazole pharmacokinetics and subsequently propose a dosage optimisation strategy. A prospective multi-centre study in adult intensive care unit patients receiving isavuconazole was performed. Blood samples were collected on eight timepoints over one dosing interval between days 3-7 of treatment and optionally on one timepoint after discontinuation. Total and unbound isavuconazole pharmacokinetics were analysed by means of population pharmacokinetic modelling using NONMEM. The final model was used to perform simulations to assess exposure described by the area under the concentration-time curve and propose an adaptive dosing approach. Population pharmacokinetics of total and unbound isavuconazole were best described by an allometrically scaled two-compartment model with a saturable protein-binding model and interindividual variability on clearance and the maximum binding capacity. The median (range) isavuconazole unbound fraction was 1.65% (0.83-3.25%). After standard dosing, only 35.8% of simulated patients reached a total isavuconazolearea under the concentration-time curve > 60mg·h/L at day 14. The proposed adaptive dosing strategy resulted in an increase to 62.3% of patients at adequate steady-state exposure. In critically ill patients, total isavuconazole exposure is reduced and protein binding is highly variable. We proposed an adaptive dosing approach to enhance early treatment optimisation in this high-risk population. ClinicalTrials.gov identifier: NCT04777058.

The aim of this study was to develop a population pharmacokinetic model of digoxin in patients over 90 years old and to propose an equation for adjusting digoxin dose in this population. We included 326 nonagenarian patients admitted to Severo Ochoa University Hospital (Spain) who received digoxin and were under therapeutic drug monitoring. All data were retrospectively collected, and population modeling was performed with non-linear mixed-effect modeling software (NONMEM®). One- and two-compartment models were tested to calculate digoxin clearance (Cl), volume of distribution (Vd), absorption rate constant (Ka), and bioavailability (bioavailable fraction, F). The covariates were evaluated by stepwise covariate model building, and the final model was internally validated by bootstrap analysis with 1000 resamples. External validation was performed with another population of 95 patients with the same characteristics as the modeling group. The population was 26% males, with a mean age of 93.2 years (90-103 years), mean creatinine 1.11 mg/dL (0.42-3.81 mg/dL), and mean total body weight 61.2 kg (40-100 kg). The pharmacokinetics of digoxin were best described by a one-compartment model (ADVAN2 TRANS2), with first-order conditional estimation with interaction. The covariates with influence on our model were creatinine clearance based on the Cockcroft-Gault equation (CG), serum potassium (K), co-administration of loop diuretics, and sex: Cl/F = 4.55 · (CG/36.4)0.468 · 0.83LD · 1.21SEX; Vd/F = 355 · (K/4.3)-0.849; Ka = 1.22 h-1 [where LD indicates loop diuretics (1 for administered, 0 for otherwise) and SEX indicates patient sex (1 for male, 0 for female)]. Based on our results, we proposed an equation to adjust the digoxin dosing regimen in nonagenarian patients: dose (mg) = 0.144 · (CG/36.4)0.468 · 0.83LD · 1.21SEX. The greatest influence on digoxin clearance came from renal function calculated by the Cockcroft-Gault equation. Vd was decreased by K. The model developed showed a precise predictive performance to be applied for therapeutic drug monitoring.

Precision medicine requires individualized modeling of disease and drug dynamics, with machine learning-based computational techniques gaining increasing popularity. The complexity of either field, however, makes current pharmacological problems opaque to machine learning practitioners, and state-of-the-art machine learning methods inaccessible to pharmacometricians. To help bridge the two worlds, we provide an introduction to current problems and techniques in pharmacometrics that ranges from pharmacokinetic and pharmacodynamic modeling to pharmacometric simulations, model-informed precision dosing, and systems pharmacology, and review some of the machine learning approaches to address them. We hope this would facilitate collaboration between experts, with complementary strengths of principled pharmacometric modeling and flexibility of machine learning leading to synergistic effects in pharmacological applications.

Zibotentan, a selective endothelin A receptor antagonist, is in development for chronic liver and kidney disease. The pharmacokinetics (PK) of zibotentan were previously investigated in patients with either renal impairment or hepatic impairment, but the impact of both pathologies on PK was not evaluated. This study evaluated the PK and tolerability of a single oral dose of zibotentan in participants with concurrent moderate renal impairment and moderate hepatic impairment versus control participants. Twelve participants with moderate renal and hepatic impairment and 11 healthy matched control participants with no clinically significant liver or kidney disease were enrolled in an open-label, parallel-group study design. After administration of a single oral dose of zibotentan 5 mg, blood and urine sampling was performed. Pharmacokinetic parameters were determined for each of the two cohorts and compared. Comparisons between the cohorts were based on the geometric least squares mean ratio for the primary endpoints, which were area under the plasma concentration-time curve (AUC) from time zero to infinity (AUC∞) and from time zero to the time of the last measurable concentration (AUClast), and maximum plasma drug concentration (Cmax) on Day 1 through 120 h post-dose. Secondary endpoints included apparent total body clearance (CL/F) on Day 1 through 120 h post-dose. Safety endpoints were assessed up to discharge. In total, 11 participants with concurrent moderate renal and hepatic impairment, and 11 controls, completed the study. Zibotentan was generally well tolerated, and no new clinically significant safety findings were observed. Total exposure (AUC∞ and AUClast) was approximately 2.10-fold higher in participants with concurrent moderate renal and hepatic impairment versus controls, while Cmax and total nonrenal body clearance were similar among all groups. A regression-based post hoc analysis, comparing exposure and CL/F in patients with concurrent impairment to patients with either renal or hepatic impairment alone, showed that CL/F with concurrent impairment was approximately half of that in controls and was positively correlated with reduction of renal function. Inclusion of the data on concurrent moderate renal and hepatic impairment in the regression analysis led to a narrower confidence interval for the predicted mean CL/F in participants with moderate hepatic impairment. The presented findings advance the understanding of the PK of zibotentan in both renal impairment and hepatic impairment, with and without overlapping pathologies, and will thus increase the confidence of dose selection in future studies, particularly in vulnerable patient populations with concurrent renal and hepatic impairment. ClinicalTrials.gov identifier: NCT05112419.

Ponesimod, a selective, rapidly reversible, and orally active, sphingosine-1 phosphate receptor (S1P) modulator, is indicated for the treatment of relapsing-remitting multiple sclerosis (RRMS). The clinical pharmacokinetics (PK) and pharmacodynamics (PD) of ponesimod was studied in 16 phase I, one phase II, and one phase III clinical studies. Ponesimod population PK was characterized by an open two-compartment disposition model with a terminal half-life of 33h (accumulation factor of 2- to 2.6-fold), and fast and almost complete oral absorption (absolute oral bioavailability: 84%), reaching peak plasma and blood concentrations within 2-4h. Ponesimod is highly metabolized, and the parent compound along with its two major (non-clinically active) metabolites are mainly excreted in the feces (recovery: 57.3-79.6%) and to a lesser extent in the urine (recovery: 10.3-18.4%). Additionally, the population PKPD model characterized the ponesimod effects on heart rate: a transient, dose-dependent decrease in heart rate in the first days of dosing, that is mitigated by administering the first doses of ponesimod treatment using a gradual up-titration schedule, before reaching the daily maintenance dose of 20mg. This selected maintenance dose has been shown to be superior in reducing annualized relapse rate (ARR) when compared with teriflunomide in a pivotal phase III study. Furthermore, a dose-dependent reduction of peripheral lymphocyte counts that is sustained with continued daily oral dosing of ponesimod and is rapidly (4-7days) reversible upon drug discontinuation has been characterized with an indirect response model.

Escitalopram and sertraline are commonly prescribed for anxiety and depressive disorders in children and adolescents. The pharmacokinetics (PK) of these medications have been evaluated in adults and demonstrate extensive variability, but studies in pediatric patients are limited. Therefore, we performed a population PK analysis for escitalopram and sertraline in children and adolescents to characterize the effects of demographic, clinical, and pharmacogenetic factors on drug exposure. A PK dataset was generated by extracting data from the electronic health record and opportunistic sampling of escitalopram- and sertraline-treated psychiatrically hospitalized pediatric patients aged 5-18 years. A population PK analysis of escitalopram and sertraline was performed using NONMEM. Concentration-time profiles were simulated using MwPharm++ to evaluate how covariates included in the final models influence medication exposure and compared to adult therapeutic reference ranges. The final escitalopram cohort consisted of 315 samples from 288 patients, and the sertraline cohort consisted of 265 samples from 255 patients. A one-compartment model with a proportional residual error model best described the data for both medications. For escitalopram, CYP2C19 phenotype and concomitant CYP2C19 inhibitors affected apparent clearance (CL/F), and normalizing CL/F and apparent volume of distribution (V/F) to body surface area (BSA) improved estimations. The final escitalopram model estimated CL/F and V/F at 14.2 L/h/1.73 m2 and 428 L/1.73 m2, respectively. For sertraline, CYP2C19 phenotype and concomitant CYP2C19 inhibitors influenced CL/F, and empirical allometric scaling of patient body weight on CL/F and V/F was significant. The final sertraline model estimated CL/F and V/F at 124 L/h/70 kg and 4320 L/70 kg, respectively. Normalized trough concentrations (Ctrough) for CYP2C19 poor metabolizers taking escitalopram were 3.98-fold higher compared to normal metabolizers (151.1 ng/mL vs 38.0 ng/mL, p < 0.0001), and normalized Ctrough for CYP2C19 poor metabolizers taking sertraline were 3.23-fold higher compared to normal, rapid, and ultrarapid metabolizers combined (121.7 ng/mL vs 37.68 ng/mL, p < 0.0001). Escitalopram- and sertraline-treated poor metabolizers may benefit from a dose reduction of 50-75% and 25-50%, respectively, to normalize exposure to other phenotypes. To our knowledge, this is the largest population PK analysis of escitalopram and sertraline in pediatric patients. Significant PK variability for both medications was observed and was largely explained by CYP2C19 phenotype. Slower CYP2C19 metabolizers taking escitalopram or sertraline may benefit from dose reductions given increased exposure.