Is It Time to Use Modeling of Cellular Transporter Homeostasis to Inform Drug-Drug Interaction Studies: Theoretical Considerations.

Mathematical modeling has been an important tool in pharmaceutical research for 50 + years and there is increased emphasis over the last decade on using modeling to improve the efficiency and effectiveness of drug development. In an earlier commentary, we applied a multiscale model linking 6 scales (whole body, tumor, vasculature, cell, spatial location, time), together with literature data on nanoparticle and tumor properties, to demonstrate the effects of nanoparticle particles on systemic disposition. The current commentary used a 4-scale model (cell membrane, intracellular organelles, spatial location, time) together with literature data on the intracellular processing of membrane receptors and transporters to demonstrate disruption of transporter homeostasis can lead to drug-drug interaction (DDI) between victim drug (VD) and perpetrator drug (PD), including changes in the area-under-concentration-time-curve of VD in cells that are considered significant by the US Food and Drug Administration (FDA). The model comprised 3 computational components: (a) intracellular transporter homeostasis, (b) pharmacokinetics of extracellular and intracellular VD/PD concentrations, and (c) pharmacodynamics of PD-induced stimulation or inhibition of an intracellular kinetic process. Model-based simulations showed that (a) among the five major endocytic processes, perturbation of transporter internalization or recycling led to the highest incidence and most extensive DDI, with minor DDI for perturbing transporter synthesis and early-to-late endosome and no DDI for perturbing transporter degradation and (b) three experimental conditions (spatial transporter distribution in cells, VD/PD co-incubation time, extracellular PD concentrations) were determinants of DDI detection. We propose modeling is a useful tool for hypothesis generation and for designing experiments to identify potential DDI; its application further aligns with the model-informed drug development paradigm advocated by FDA.

Quantitative Systems Pharmacology Modeling of Avadomide-Induced Neutropenia Enables Virtual Clinical Dose and Schedule Finding Studies.

Avadomide is a cereblon E3 ligase modulator and a potent antitumor and immunomodulatory agent. Avadomide trials are challenged by neutropenia as a major adverse event and a dose-limiting toxicity. Intermittent dosing schedules supported by preclinical data provide a strategy to reduce frequency and severity of neutropenia; however, the identification of optimal dosing schedules remains a clinical challenge. Quantitative systems pharmacology (QSP) modeling offers opportunities for virtual screening of efficacy and toxicity levels produced by alternative dose and schedule regimens, thereby supporting decision-making in translational drug development. We formulated a QSP model to capture the mechanism of avadomide-induced neutropenia, which involves cereblon-mediated degradation of transcription factor Ikaros, resulting in a maturation block of the neutrophil lineage. The neutropenia model was integrated with avadomide-specific pharmacokinetic and pharmacodynamic models to capture dose-dependent effects. Additionally, we generated a disease-specific virtual patient population to represent the variability in patient characteristics and response to treatment observed for a diffuse large B-cell lymphoma trial cohort. Model utility was demonstrated by simulating the avadomide effect in the virtual population for various dosing schedules and determining the incidence of high-grade neutropenia, its duration, and the probability of recovery to low-grade neutropenia.

Target Site Delivery and Residence of Nanomedicines: Application of Quantitative Systems Pharmacology.

Quantitative systems pharmacology (QSP), an emerging field that entails using modeling and computation to interpret, interrogate, and integrate drug effects spanning from the molecule to the whole organism to forecast treatment outcomes, is expected to enhance the efficiency of drug development. Since late 2017, the U.S. Food and Drug Administration has advocated the use of an analogous approach of model-informed drug development. This review focuses on issues pertaining to nanosized medicines (NP) and the potential utility of QSP to determine NP delivery and residence at extracellular or intracellular targets in vivo. The kinetic processes governing NP disposition and transport, interactions with biologic matrix components, binding and internalization in cells, and intracellular trafficking are determined, sometimes jointly, by NP properties (e.g., dimension, materials, surface charge and modifications, shape, and geometry) and target tissue properties (e.g., perfusion status, vessel pore size and wall thickness, vessel and cell density, composition of extracellular matrix, and void volume fraction). These various determinants, together with the heterogeneous tissue structures and microenvironment factors in solid tumors, lead to environment-, spatial-, and time-dependent changes in NP concentrations that are difficult to predict. Adding to the complexity is the recent discovery that NP surface-coating protein corona, whose composition depends on NP properties and which undergoes continuous evolution with time and local protein environments, is yet another unpredictable variable. Examples are provided to demonstrate the potential utility of QSP-based multiscale modeling to capture the physicochemical and biologic processes in equations to enable computational studies of the key kinetic processes in cancer treatments.

Systemic Bioequivalence Is Unlikely to Equal Target Site Bioequivalence for Nanotechnology Oncologic Products.

Approval of generic drugs by the US Food and Drug Administration (FDA) requires the product to be pharmaceutically equivalent to the reference listed drug (RLD) and demonstrate bioequivalence (BE) in effectiveness when administered to patients under the conditions in the RLD product labeling. Effectiveness is determined by drug exposure at the target sites. However, since such measurement is usually unavailable, systemic exposure is assumed to equal target site exposure and systemic BE to equal target site BE. This assumption, while it often applies to small molecule drug products that are readily dissolved in biological fluids and systemically absorbed, is unlikely to apply to nanotechnology products (NP) that exist as heterogeneous systems and are subjected to dimension- and material-dependent changes. This commentary provides an overview of the intersecting and spatial-dependent processes and variables governing the delivery and residence of oncologic NP in solid tumors. In order to provide a quantitative perspective of the collective effects of these processes, we used quantitative systems pharmacology (QSP) multi-scale modeling to capture the physicochemical and biological events on several scales (whole-body, organ/suborgan, cell/subcellular, spatial locations, time). QSP is an emerging field that entails using modeling and computation to facilitate drug development; an analogous approach (i.e., model-informed drug development) is advocated by to FDA. The QSP model-based simulations illustrated that small changes in NP attributes (e.g., size variations during manufacturing, interactions with proteins in biological milieu) could lead to disproportionately large differences in target site exposure, rending systemic BE unlikely to equal target site BE.