Fifty-Eight Years and Counting: High-Impact Publishing in Computational Pharmaceutical Sciences and Mechanism-Based Modeling.

With this issue of the Journal of Pharmaceutical Sciences, we celebrate the nearly 6 decades of contributions to mechanistic-based modeling and computational pharmaceutical sciences. Along with its predecessor, The Journal of the American Pharmaceutical Association: Scientific Edition first published in 1911, JPharmSci has been a leader in the advancement of pharmaceutical sciences beginning with its inaugural edition in 1961. As one of the first scientific journals focusing on pharmaceutical sciences, JPharmSci has established a reputation for publishing high-quality research articles using computational methods and mechanism-based modeling. The journal's publication record is remarkable. With over 15,000 articles, 3000 notes, and more than 650 reviews from industry, academia, and regulatory agencies around the world, JPharmSci has truly been the leader in advancing pharmaceutical sciences.

Sensitive, High-Throughput, and Robust Trapping-Micro-LC-MS Strategy for the Quantification of Biomarkers and Antibody Biotherapeutics.

For LC-MS-based targeted quantification of biotherapeutics and biomarkers in clinical and pharmaceutical environments, high sensitivity, high throughput, and excellent robustness are all essential but remain challenging. For example, though nano-LC-MS has been employed to enhance analytical sensitivity, it falls short because of its low loading capacity, poor throughput, and low operational robustness. Furthermore, high chemical noise in protein bioanalysis typically limits the sensitivity. Here we describe a novel trapping-micro-LC-MS (T-µLC-MS) strategy for targeted protein bioanalysis, which achieves high sensitivity with exceptional robustness and high throughput. A rapid, high-capacity trapping of biological samples is followed by µLC-MS analysis; dynamic sample trapping and cleanup are performed using pH, column chemistry, and fluid mechanics separate from the µLC-MS analysis, enabling orthogonality, which contributes to the reduction of chemical noise and thus results in improved sensitivity. Typically, the selective-trapping and -delivery approach strategically removes >85% of the matrix peptides and detrimental components, markedly enhancing sensitivity, throughput, and operational robustness, and narrow-window-isolation selected-reaction monitoring further improves the signal-to-noise ratio. In addition, unique LC-hardware setups and flow approaches eliminate gradient shock and achieve effective peak compression, enabling highly sensitive analyses of plasma or tissue samples without band broadening. In this study, the quantification of 10 biotherapeutics and biomarkers in plasma and tissues was employed for method development. As observed, a significant sensitivity gain (up to 25-fold) compared with that of conventional LC-MS was achieved, although the average run time was only 8 min/sample. No appreciable peak deterioration or loss of sensitivity was observed after >1500 injections of tissue and plasma samples. The developed method enabled, for the first time, ultrasensitive LC-MS quantification of low levels of a monoclonal antibody and antigen in a tumor and cardiac troponin I in plasma after brief cardiac ischemia. This strategy is valuable when highly sensitive protein quantification in large sample sets is required, as is often the case in typical biomarker validation and pharmaceutical investigations of antibody therapeutics.

Physiologically-based pharmacokinetic modeling to predict the clinical pharmacokinetics of monoclonal antibodies.

Accurate prediction of the clinical pharmacokinetics of new therapeutic entities facilitates decision making during drug discovery, and increases the probability of success for early clinical trials. Standard strategies employed for predicting the pharmacokinetics of small-molecule drugs (e.g., allometric scaling) are often not useful for predicting the disposition monoclonal antibodies (mAbs), as mAbs frequently demonstrate species-specific non-linear pharmacokinetics that is related to mAb-target binding (i.e., target-mediated drug disposition, TMDD). The saturable kinetics of TMDD are known to be influenced by a variety of factors, including the sites of target expression (which determines the accessibility of target to mAb), the extent of target expression, the rate of target turnover, and the fate of mAb-target complexes. In most cases, quantitative information on the determinants of TMDD is not available during early phases of drug discovery, and this has complicated attempts to employ mechanistic mathematical models to predict the clinical pharmacokinetics of mAbs. In this report, we introduce a simple strategy, employing physiologically-based modeling, to predict mAb disposition in humans. The approach employs estimates of inter-antibody variability in rate processes of extravasation in tissues and fluid-phase endocytosis, estimates for target concentrations in tissues derived through use of categorical immunohistochemical scores, and in vitro measures of the turnover of target and target-mAb complexes. Monte Carlo simulations were performed for four mAbs (cetuximab, figitumumab, dalotuzumab, trastuzumab) directed against three targets (epidermal growth factor receptor, insulin-like growth factor receptor 1, human epidermal growth factor receptor 2). The proposed modeling strategy was able to predict well the pharmacokinetics of cetuximab, dalotuzumab, and trastuzumab at a range of doses, but trended towards underprediction of figitumumab concentrations, particularly at high doses. The general agreement between model predictions and experimental observations suggests that PBPK modeling may be useful for the a priori prediction of the clinical pharmacokinetics of mAb therapeutics.

Scale-up of a physiologically-based pharmacokinetic model to predict the disposition of monoclonal antibodies in monkeys.

Preclinical assessment of monoclonal antibody (mAb) disposition during drug development often includes investigations in non-human primate models. In many cases, mAb exhibit non-linear disposition that relates to mAb-target binding [i.e., target-mediated disposition (TMD)]. The goal of this work was to develop a physiologically-based pharmacokinetic (PBPK) model to predict non-linear mAb disposition in plasma and in tissues in monkeys. Physiological parameters for monkeys were collected from several sources, and plasma data for several mAbs associated with linear pharmacokinetics were digitized from prior literature reports. The digitized data displayed great variability; therefore, parameters describing inter-antibody variability in the rates of pinocytosis and convection were estimated. For prediction of the disposition of individual antibodies, we incorporated tissue concentrations of target proteins, where concentrations were estimated based on categorical immunohistochemistry scores, and with assumed localization of target within the interstitial space of each organ. Kinetics of target-mAb binding and target turnover, in the presence or absence of mAb, were implemented. The model was then employed to predict concentration versus time data, via Monte Carlo simulation, for two mAb that have been shown to exhibit TMD (2F8 and tocilizumab). Model predictions, performed a priori with no parameter fitting, were found to provide good prediction of dose-dependencies in plasma clearance, the areas under plasma concentration versu time curves, and the time-course of plasma concentration data. This PBPK model may find utility in predicting plasma and tissue concentration versus time data and, potentially, the time-course of receptor occupancy (i.e., mAb-target binding) to support the design and interpretation of preclinical pharmacokinetic-pharmacodynamic investigations in non-human primates.

AAPS workshop report: strategies to address therapeutic protein-drug interactions during clinical development.

Therapeutic proteins (TPs) are increasingly combined with small molecules and/or with other TPs. However preclinical tools and in vitro test systems for assessing drug interaction potential of TPs such as monoclonal antibodies, cytokines and cytokine modulators are limited. Published data suggests that clinically relevant TP-drug interactions (TP-DI) are likely from overlap in mechanisms of action, alteration in target and/or drug-disease interaction. Clinical drug interaction studies are not routinely conducted for TPs because of the logistical constraints in study design to address pharmacokinetic (PK)- and pharmacodynamic (PD)-based interactions. Different pharmaceutical companies have developed their respective question- and/or risk-based approaches for TP-DI based on the TP mechanism of action as well as patient population. During the workshop both company strategies and regulatory perspectives were discussed in depth using case studies; knowledge gaps and best practices were subsequently identified and discussed. Understanding the functional role of target, target expression and their downstream consequences were identified as important for assessing the potential for a TP-DI. Therefore, a question-and/or risk-based approach based upon the mechanism of action and patient population was proposed as a reasonable TP-DI strategy. This field continues to evolve as companies generate additional preclinical and clinical data to improve their understanding of possible mechanisms for drug interactions. Regulatory agencies are in the process of updating their recommendations to sponsors regarding the conduct of in vitro and in vivo interaction studies for new drug applications (NDAs) and biologics license applications (BLAs).

Sensitive high performance liquid chromatographic assay for assessment of doxorubicin pharmacokinetics in mouse plasma and tissues.

A sensitive high performance liquid chromatography method (HPLC) has been developed for the quantification of doxorubicin in mouse plasma and tissues. Samples of serum or tissue homogenates, 20 microl, were analyzed following a single step protein precipitation using perchloric acid (35%, v/v). Doxorubicin was separated from the internal standard, daunorubicin, on a Zorbax 300SB C(18) column at 35 degrees C. Mobile phase was comprised of acetonitrile and water (25:75) containing 0.1% triethylamine, and was adjusted to pH 3 with phosphoric acid. Peaks eluting from the column were detected with a fluorescence detector with excitation and emission wavelengths of 480 and 560 nm, respectively. Standard curves were linear in the range 5-1000 ng/ml, and correlation coefficients were typically greater than 0.999. Intra-assay recoveries ranged from 94.7 to 99.9%, and inter-assay recoveries were in the range of 95.2-101%. The associated coefficient of variation (CV) was less than 10% in all cases. The method was successfully applied to investigate doxorubicin plasma pharmacokinetics and tissue distribution in athymic Fox(nu) mice.