Ben-Fillippo Krippendorff

Nonlinear pharmacokinetics of therapeutic proteins resulting from receptor mediated endocytosis.

Receptor mediated endocytosis (RME) plays a major role in the disposition of therapeutic protein drugs in the body. It is suspected to be a major source of nonlinear pharmacokinetic behavior observed in clinical pharmacokinetic data. So far, mostly empirical or semi-mechanistic approaches have been used to represent RME. A thorough understanding of the impact of the properties of the drug and of the receptor system on the resulting nonlinear disposition is still missing, as is how to best represent RME in pharmacokinetic models. In this article, we present a detailed mechanistic model of RME that explicitly takes into account receptor binding and trafficking inside the cell and that is used to derive reduced models of RME which retain a mechanistic interpretation. We find that RME can be described by an extended Michaelis–Menten model that accounts for both the distribution and the elimination aspect of RME. If the amount of drug in the receptor system is negligible a standard Michaelis–Menten model is capable of describing the elimination by RME. Notably, a receptor system can efficiently eliminate drug from the extracellular space even if the total number of receptors is small. We find that drug elimination by RME can result in substantial nonlinear pharmacokinetics. The extent of nonlinearity is higher for drug/receptor systems with higher receptor availability at the membrane, or faster internalization and degradation of extracellular drug. Our approach is exemplified for the epidermal growth factor receptor system.

Mechanism-Based Inhibition: Deriving KI and kinact Directly from Time-Dependent IC50 Values

The potential of enzyme inhibition of a drug is frequently quantified in terms of IC50 values. Although this is a suitable quantity for reversible inhibitors, concerns arise when dealing with irreversible or mechanism-based inhibitors (MBIs). IC50 values of MBIs are time dependent, causing serious problems when aiming at ranking different compounds with respect to their inhibitory potential. As a consequence, most studies and ranking schemes related to MBIs rely on the inhibition constant (KI) and the rate of enzyme inactivation (kinact) rather than on IC50 values. In this article, the authors derive a novel relation between potentially time-dependent IC 50 values and KI, kinact parameters for different types of inhibition. This allows for direct estimation of KI and kinact values from time-dependent IC50 values, even without the need of additional preincubation experiments. The application of this approach is illustrated using a fluorimetric assay to access the drug-drug interaction potential associated with new chemical entities. The approach can easily be implemented using standard software tools (e.g., XLfit) and may also be suitable for applications where mechanism-based inhibition is a desired mode of action (e.g., at particular pharmacological drug targets). (Journal of Biomolecular Screening 2009:913-923)

Optimizing classification of drug-drug interaction potential for CYP450 isoenzyme inhibition assays in early drug discovery.

In drug discovery, the potential of cytochrome P450 inhibition of new chemical entities is frequently quantified in terms of IC50 values. In early drug discovery, a risk classification into low, medium, or high potential inhibitors is often sufficient for ranking and prioritizing of compounds. Although often 6 or more inhibitor concentrations are used to determine the IC50 value, the question arises whether it is possible to predict the risk class based on fewer inhibitor concentrations with comparable reliability. In this article, the authors propose a new integrated 2-point method with inhibitor concentrations chosen in accordance with the risk classification. They analyze its predictive power and the feasibility of not only classifying the compounds into different risk classes but also ranking those compounds that have been binned into the middle risk class. The proposed integrated 2-point method is thus highly suitable for automation. Altogether, it maintains the quality of the prediction while considerably reducing time and cost. The proposed method is applicable to other IC50 assays and risk classifications.

Mechanism-based inhibition: deriving K(I) and k(inact) directly from time-dependent IC(50) values.

The potential of enzyme inhibition of a drug is frequently quantified in terms of IC50 values. Although this is a suitable quantity for reversible inhibitors, concerns arise when dealing with irreversible or mechanism-based inhibitors (MBIs). IC50 values of MBIs are time dependent, causing serious problems when aiming at ranking different compounds with respect to their inhibitory potential. As a consequence, most studies and ranking schemes related to MBIs rely on the inhibition constant (KI) and the rate of enzyme inactivation (kinact) rather than on IC50 values. In this article, the authors derive a novel relation between potentially time-dependent IC 50 values and KI, kinact parameters for different types of inhibition. This allows for direct estimation of KI and kinact values from time-dependent IC50 values, even without the need of additional preincubation experiments. The application of this approach is illustrated using a fluorimetric assay to access the drug-drug interaction potential associated with new chemical entities. The approach can easily be implemented using standard software tools (e.g., XLfit) and may also be suitable for applications where mechanism-based inhibition is a desired mode of action (e.g., at particular pharmacological drug targets). (Journal of Biomolecular Screening 2009:913-923)

Anti-tumour efficacy of capecitabine in a genetically engineered mouse model of pancreatic cancer.

Capecitabine (CAP) is a 5-FU pro-drug approved for the treatment of several cancers and it is used in combination with gemcitabine (GEM) in the treatment of patients with pancreatic adenocarcinoma (PDAC). However, limited pre-clinical data of the effects of CAP in PDAC are available to support the use of the GEMCAP combination in clinic. Therefore, we investigated the pharmacokinetics and the efficacy of CAP as a single agent first and then in combination with GEM to assess the utility of the GEMCAP therapy in clinic. Using a model of spontaneous PDAC occurring in KrasG12D; p53R172H; Pdx1-Cre (KPC) mice and subcutaneous allografts of a KPC PDAC-derived cell line (K8484), we showed that CAP achieved tumour concentrations (∼25 µM) of 5-FU in both models, as a single agent, and induced survival similar to GEM in KPC mice, suggesting similar efficacy. In vitro studies performed in K8484 cells as well as in human pancreatic cell lines showed an additive effect of the GEMCAP combination however, it increased toxicity in vivo and no benefit of a tolerable GEMCAP combination was identified in the allograft model when compared to GEM alone. Our work provides pre-clinical evidence of 5-FU delivery to tumours and anti-tumour efficacy following oral CAP administration that was similar to effects of GEM. Nevertheless, the GEMCAP combination does not improve the therapeutic index compared to GEM alone. These data suggest that CAP could be considered as an alternative to GEM in future, rationally designed, combination treatment strategies for advanced pancreatic cancer.

Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling

Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity.

Abstract 1924: The aurora kinase inhibitor CYC3 synergizes with low concentrations of paclitaxel in pancreatic cancer cells in vitro

Proceedings: AACR 103rd Annual Meeting 2012‐‐ Mar 31‐Apr 4, 2012; Chicago, IL CYC3, an Aurora Kinase A specific inhibitor, suppresses the pancreatic cancer cell growth (72h GI50 2.4μM in MiaPaCa-2 cells and 1.95μM in Panc-1 cells), arrest the cells at M phase and induces apoptosis. In order to evaluate CYC3s clinical potential, we employed mathematical models and simulations to look for possible synergistic combinations of CYC3 with paclitaxel in MiaPaCa-2 and Panc-1 cells. We have identified low concentrations of paclitaxel (3nM) and CYC3 is synergistic in inhibiting pancreatic cell growth, achieving similar effects as high concentrations of paclitaxel (30nM). Liquid Chromatography-Mass Spectrometry (LC-MS) analysis shows that CYC3 does not alter the cellular uptake of paclitaxel, supporting a synergistic mechanism at molecular level. In addition, the combination of CYC3 with low concentration of paclitaxel (3nM) displays less myelotoxicity compared to high concentrations of paclitaxel (30nM) in colony formation assay using Colony Forming Unit of Granulocyte and Macrophage (CFU-GM) (60-70% vs 100% inhibition), suggesting a potentially safer but equally efficient application of paclitaxel in patients. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1924. doi:1538-7445.AM2012-1924

Mechanism-Based Inhibition: Deriving KIand kinactDirectly from Time-Dependent IC50Values

The potential of enzyme inhibition of a drug is frequently quantified in terms of IC50 values. Although this is a suitable quantity for reversible inhibitors, concerns arise when dealing with irreversible or mechanism-based inhibitors (MBIs). IC50 values of MBIs are time dependent, causing serious problems when aiming at ranking different compounds with respect to their inhibitory potential. As a consequence, most studies and ranking schemes related to MBIs rely on the inhibition constant (KI) and the rate of enzyme inactivation (kinact) rather than on IC50 values. In this article, the authors derive a novel relation between potentially time-dependent IC 50 values and KI, kinact parameters for different types of inhibition. This allows for direct estimation of KI and kinact values from time-dependent IC50 values, even without the need of additional preincubation experiments. The application of this approach is illustrated using a fluorimetric assay to access the drug-drug interaction potential associated with new chemical entities. The approach can easily be implemented using standard software tools (e.g., XLfit) and may also be suitable for applications where mechanism-based inhibition is a desired mode of action (e.g., at particular pharmacological drug targets). (Journal of Biomolecular Screening 2009:913-923)