Concentration-QTc modeling of sitravatinib in patients with advanced solid malignancies.

Sitravatinib (MGCD516) is an orally available, small molecule, tyrosine kinase inhibitor that has been evaluated in patients with advanced solid tumors. Concentration-corrected QT interval (QTc; C-QTc) modeling was undertaken, using 767 matched concentration-ECG observations from 187 patients across two clinical studies in patients with advanced solid malignancies, across a dose range of 10-200 mg, via a linear mixed-effects (LME) model. The effect on heart rate (HR)-corrected QT interval via Fridericia's correction method (QTcF) at the steady-state maximum concentration (Cmax,ss) for the sitravatinib proposed therapeutic dosing regimen (100 mg malate once daily [q.d.]) without and with relevant intrinsic and extrinsic factors were predicted. No significant changes in HR from baseline were observed. Hysteresis between sitravatinib plasma concentration and change in QTcF from baseline (ΔQTcF) was not observed. There was no significant relationship between sitravatinib plasma concentration and ΔQTcF. The final C-QTc model predicted a mean (90% confidence interval [CI]) ΔQTcF of 3.92 (1.95-5.89) ms and 2.94 (0.23-6.10) ms at the proposed therapeutic dosing regimen in patients with normal organ function (best case scenario) and patients with hepatic impairment (worst-case scenario), respectively. The upper bounds of the 90% CIs were below the regulatory threshold of concern of 10 ms. The results of the described C-QTc analysis, along with corroborating results from nonclinical safety pharmacology studies, indicate that sitravatinib has a low risk of QTc interval prolongation at the proposed therapeutic dose of 100 mg malate q.d.

Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP® Technology.

Dermatophytosis is a superficial fungal infection with an ever-increasing number of patients. Culture-based mycology remains the most commonly used diagnosis, but it takes around four weeks to identify the causative agent. Therefore, routine clinical laboratories need rapid, high throughput, and accurate species-specific analytical methods for diagnosis and therapeutic management. Based on these requirements, we investigated the feasibility of DendrisCHIP® technology as an innovative molecular diagnostic method for the identification of a subset of 13 pathogens potentially responsible for dermatophytosis infections in clinical samples. This technology is based on DNA microarray, which potentially enables the detection and discrimination of several germs in a single sample. A major originality of DendrisCHIP® technology is the use of a decision algorithm for probability presence or absence of pathogens based on machine learning methods. In this study, the diagnosis of dermatophyte infection was carried out on more than 284 isolates by conventional microbial culture and DendrisCHIP®DP, which correspond to the DendrisCHIP® carrying oligoprobes of the targeted pathogens implicated in dermatophytosis. While convergence ranging from 75 to 86% depending on the sampling procedure was obtained with both methods, the DendrisCHIP®DP proved to identify more isolates with pathogens that escaped the culture method. These results were confirmed at 86% by a third method, which was either a specific RT-PCR or genome sequencing. In addition, diagnostic results with DendrisCHIP®DP can be obtained within a day. This faster and more accurate identification of fungal pathogens with DendrisCHIP®DP enables the clinician to quickly and successfully implement appropriate antifungal treatment to prevent the spread and elimination of dermatophyte infection. Taken together, these results demonstrate that this technology is a very promising method for routine diagnosis of dermatophytosis.

The DendrisCHIP® Technology as a New, Rapid and Reliable Molecular Method for the Diagnosis of Osteoarticular Infections.

Osteoarticular infections are major disabling diseases that can occur after orthopedic implant surgery in patients. The management of these infections is very complex and painful, requiring surgical intervention in combination with long-term antibiotic treatment. Therefore, early and accurate diagnosis of the causal pathogens is essential before formulating chemotherapeutic regimens. Although culture-based microbiology remains the most common diagnosis of osteoarticular infections, its regular failure to identify the causative pathogen as well as its long-term modus operandi motivates the development of rapid, accurate, and sufficiently comprehensive bacterial species-specific diagnostics that must be easy to use by routine clinical laboratories. Based on these criteria, we reported on the feasibility of our DendrisCHIP® technology using DendrisCHIP®OA as an innovative molecular diagnostic method to diagnose pathogen bacteria implicated in osteoarticular infections. This technology is based on the principle of microarrays in which the hybridization signals between oligoprobes and complementary labeled DNA fragments from isolates queries a database of hybridization signatures corresponding to a list of pre-established bacteria implicated in osteoarticular infections by a decision algorithm based on machine learning methods. In this way, this technology combines the advantages of a PCR-based method and next-generation sequencing (NGS) while reducing the limitations and constraints of the two latter technologies. On the one hand, DendrisCHIP®OA is more comprehensive than multiplex PCR tests as it is able to detect many more germs on a single sample. On the other hand, this method is not affected by the large number of nonclinically relevant bacteria or false positives that characterize NGS, as our DendrisCHIP®OA has been designed to date to target only a subset of 20 bacteria potentially responsible for osteoarticular infections. DendrisCHIP®OA has been compared with microbial culture on more than 300 isolates and a 40% discrepancy between the two methods was found, which could be due in part but not solely to the absence or poor identification of germs detected by microbial culture. We also demonstrated the reliability of our technology in correctly identifying bacteria in isolates by showing a convergence (i.e., same bacteria identified) with NGS superior to 55% while this convergence was only 32% between NGS and microbial culture data. Finally, we showed that our technology can provide a diagnostic result in less than one day (technically, 5 h), which is comparatively faster and less labor intensive than microbial cultures and NGS.

Population pharmacokinetics and exposure-response analyses of teduglutide in adult and pediatric patients with short bowel syndrome.

Teduglutide is a recombinant analog of human glucagon-like peptide-2 that regulates the functional and structural integrity of the cells lining the gastrointestinal tract. Teduglutide is approved for the treatment of patients with short bowel syndrome (SBS) who are dependent on parenteral support (PS). Population pharmacokinetic (PK) and exposure-response analyses were performed to support teduglutide dosing in patients with SBS. The analysis included 219 patients with SBS (aged <1 year, 5 patients; 1-11 years, 86 patients; 12-17 years, 8 patients; 18-79 years, 120 patients), and 259 non-SBS subjects (including healthy volunteers and subjects with renal or liver impairment). A one-compartment model with first-order absorption and linear elimination adequately characterized the PKs of teduglutide. In patients with SBS, the apparent clearance (CL/F), volume of distribution (V/F), and elimination half-life of teduglutide were 16.0 L/h, 33.9 L, and 1.47 h, respectively. CL/F depended on body weight and renal function, and V/F depended on body weight and age. Maximum concentration (Cmax ) of teduglutide was similar in adult and pediatric patients, and in Japanese and non-Japanese patients. A time- and exposure-response model dependent on the Cmax of teduglutide adequately characterized the reduction in PS over more than 2 years of treatment. Daily dosing of 0.05 mg/kg teduglutide resulted in a maximum reduction in PS of 5.76 L/week. Higher Cmax values were associated with a more important reduction in PS over time. Adult and pediatric patients with SBS presented similar PKs and response to teduglutide.

Population pharmacokinetics of bevacizumab in cancer patients with external validation.

BACKGROUND: Bevacizumab is approved for various cancers. This analysis aimed to comprehensively evaluate bevacizumab pharmacokinetics and the influence of patient variables on bevacizumab pharmacokinetics. METHODS: Rich and sparse bevacizumab serum concentrations were collected from Phase I through IV studies in early and metastatic cancers. Bevacizumab was given intravenously as single agent or in combination with chemotherapy for single- and multiple-dose schedules. RESULTS: Model-building used 8943 bevacizumab concentrations from 1792 patients with colon/colorectal, non-small cell lung, kidney, pancreatic, breast, prostate and brain cancer. Bevacizumab doses ranged from 1 to 20 mg/kg given once every 1, 2 or 3 weeks. A two-compartment model best described the data. The population estimates of clearance (CL), central volume of distribution (V1) and half-life for a typical 70-kg patient were 9.01 mL/h, 2.88 L and 19.6 days. CL and V1 increased with body weight and were higher in males than females by 14 and 18 %, respectively. CL decreased with increasing albumin and decreasing alkaline phosphatase. The final model was externally validated using 1670 concentrations from 146 Japanese patients that were not used for model-building. Mean prediction errors were -2.1, 3.1 and 1.0 % for concentrations, CL and V1, respectively, confirming adequate predictive performance. CONCLUSIONS: A robust bevacizumab pharmacokinetic model was developed and externally validated, which may be used to simulate bevacizumab exposure to optimize dosing strategies. Asian and non-Asian patients exhibited similar bevacizumab pharmacokinetics. Given the similarity in pharmacokinetics among monoclonal antibodies, this may inform pharmacokinetic studies in different ethnic groups for other therapeutic antibodies.

Population pharmacokinetic and pharmacodynamic analyses from a 4-month intradose escalation and its subsequent 12-month dose titration studies for a human monoclonal anti-FGF23 antibody (KRN23) in adults with X-linked hypophosphatemia.

X-linked hypophosphatemia (XLH) is an inherited metabolic bone disease with abnormally elevated serum FGF23 resulting in low renal maximum threshold for phosphate reabsorption, low serum phosphate (Pi) and 1,25-dihydroxyvitamin D levels with subsequent development of short stature and skeletal deformities. KRN23 is a novel human anti-FGF23 antibody for the treatment of XLH. The pharmacokinetics (PK) and pharmacodynamics (PD) models of KRN23 were assessed following subcutaneous dosing every 28 days over an initial 4-month dose escalation (0.05-0.6 mg/kg) and a subsequent 12-month titration period (0.1-1.0 mg/kg) in XLH adults. The PK of KRN23 was described by a 1-compartmental model with first-order absorption and elimination at doses ≥0.1 mg/kg. The elimination half-life was 17.8 days. Covariates did not affect KRN23 PK. Mean peak serum Pi was attained 7-10 days after dosing and progressively increased following each of the initial 4 doses with comparable peak values attained following the sixth through tenth doses with a slight decrease thereafter. A PK-PD model with a maximum effect (Emax ) and a time-varying effective concentration to reach 50% of Emax (EC50,t ) described data adequately. Typical Emax was 1.5 mg/dL. Typical EC50,t was 1780 ng/mL and 5999 ng/mL after first and last dose, respectively.

Bevacizumab dosing strategy in paediatric cancer patients based on population pharmacokinetic analysis with external validation.

AIM: The aim of the present study was to evaluate the pharmacokinetics of bevacizumab and various dosing strategies for this agent in paediatric patients. METHODS: Data were collected from 232 paediatric patients (1971 concentrations) in five studies, with a wide range of age (0.5-21 years), body weight (BWT; 5.9-125 kg), and regimens (5-15 mg kg(-1) biweekly or triweekly). Data from 152 patients (1427 concentrations) and 80 patients (544 concentrations) were used for model building and external validation, respectively. Steady-state exposure was simulated under BWT-based, body surface area (BSA)-based, ideal body weight (IBW)-based, and tier-based doses. NONMEM and R were used for analyses. RESULTS: Typical estimates of clearance, central volume of distribution (V1), and median half-life were 9.04 ml h(-1) , 2851 ml, and 19.6 days, respectively. Clearance decreased with increasing albumin. Clearance and V1 increased with BWT and were higher in male patients. Clearance and V1 were lower in children with primary central nervous system (CNS) tumours than in children with sarcomas, resulting in 49% higher trough (C min) and 29% higher peak (Cmax) concentrations. BWT-adjusted clearance and V1 remained unchanged across ages. Paediatric C min was similar to adult C min under all dosing strategies. Paediatric Cmax exceeded adult Cmax under tier-based doses. CONCLUSIONS: BWT-adjusted pharmacokinetic parameter estimates in paediatric patients were similar to those in adults, and similar across ages. Bevacizumab exposure was higher in children with primary CNS tumours than in children with sarcomas. BSA-based, IBW-based, and tier-based doses offered no substantial advantage over the BWT-based dose currently used in adults for bevacizumab. Given the similarity in pharmacokinetics among many monoclonal antibodies, this may help to develop practical paediatric dosing guidelines for other therapeutic antibodies.

Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans.

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CL(int)) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CL(int), calculated as the ratio of the in vivo V(max) (µmol/h/kg bw rat) to the K(m) (µM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R(2) = 0.8; leave-one-out cross-validation Q(2) = 0.75) for CL(int) normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CL(int) (L PL/h/kg bw rat) from the input data on log P(ow), log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for CL(int)) to minimum (using UMCI for CL(int)) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CL(int) in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CL(int) on human pharmacokinetics of VOCs.

A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals.

The algorithms in the literature focusing to predict tissue:blood PC (P(tb)) for environmental chemicals and tissue:plasma PC based on total (K(p)) or unbound concentration (K(pu)) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P(tb), K(p) and K(pu) for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P(tb), K(p) or K(pu) of muscle (n=174), liver (n=139) and adipose tissue (n=141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.