Correlation of Tissue-Plasma Partition Coefficients Between Normal Tissues and Subcutaneous Xenografts of Human Tumor Cell Lines in Mouse as a Prediction Tool of Drug Penetration in Tumors

Naquotinib exerts antitumor activity in activated B-cell-like diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL), the most common type of B-cell non-Hodgkin lymphoma (NHL), is categorized into two major subtypes, activated B-cell-like (ABC) and germinal center B-cell-like (GCB). The ABC subtype is associated with worse prognosis than the GCB subtype using currently available therapies such as combination treatment with rituximab plus standard cytotoxic chemotherapy. The B-cell receptor (BCR) pathway is activated in ABC DLBCL, suggesting that inhibition of this pathway could provide an alternative strategy for treatment. Naquotinib is an irreversible tyrosine kinase inhibitor (TKI) originally designed to target the epidermal growth factor receptor (EGFR). As sequence alignment analysis indicates that irreversible EGFR-TKIs also inhibit Bruton's tyrosine kinase (BTK), here, we characterized the inhibitory effects of naquotinib against BTK in comparison to ibrutinib, acalabrutinib, tirabrutinib and spebrutinib. Naquotinib inhibited BTK kinase activity with similar potency to that for EGFR activating mutations. In vivo, naquotinib induced tumor regression and suppressed tumor recurrence in TMD8 and OCI-Ly10, ABC DLBCL cell line xenograft models, at a lower dose than the clinically relevant dose. Compared to other BTK inhibitors, naquotinib showed faster onset and comparable inhibition of BTK following incubation with cell lines for 3 and 20 h. In addition, naquotinib showed longer continuous inhibition of BTK following removal of the compound, lasting for at least 26 h after removal. Pharmacokinetics studies in the TMD8 xenograft model showed higher concentration and slower elimination of naquotinib in tumors than other BTK inhibitors. These data suggest that naquotinib may have therapeutic potential in ABC DLBCL patients.

Prediction of Tissue-to-Plasma Ratios of Basic Compounds in Mice

BACKGROUND

Majority of reported studies so far developed correlation regression equations using the rat muscle-to-plasma drug concentration ratio (Kp-muscle) to predict tissue-to-plasma drug concentration ratios (Kp-tissues). Use of regression equations derived from rat Kp-muscle may not be ideal to predict the mice tissue-Kps as there are species differences.

OBJECTIVES

(i) To develop the linear regression equations using mouse tissue-Kps; (ii) to assess the correlation between organ blood flow and/or organ weight with tissue-Kps and (iii) compare the observed tissue-Kps from mice with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations.

METHOD

Disposition of 12 small molecules were investigated extensively in mouse plasma and tissues after a single oral dose administration. Linear correlation was assessed for each of the tissue with rest of the other tissues, separately for weak and strong bases.

RESULT

Newly developed regression equations using mice tissue-Kps, predicted 79% data points within twofold. As observed correlation r ² range was 0.75-0.98 between Kp-muscle and Kp-brain, -spleen, -skin, -liver, -lung, suggesting superior correlation between the tissue-Kps. Order of tissue-Kps, showed that tissue concentrations were directly proportional to the organ blood flow and inversely to the organ weight. Further, the observed tissue-Kps from mice were compared with corresponding predicted tissue-Kps using Richter's rat-Kp specific equations. Overall, 46, 54 and 63% data points were under predicted (<0.5-fold) for liver, spleen and lung, respectively. Whereas 63 and 75% data points were over predicted (>twofold) for skin and brain, respectively. These findings suggest that cross species extrapolation predictability is poor.

CONCLUSION

All these findings together suggest that mouse specific regression equations developed under controlled experimental conditions could be most appropriate for predicting mouse tissue-Kps for compounds with wide range of volume of distribution.

Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models

BACKGROUND

Predicting target site drug concentrations is of key importance for rank ordering compounds before proceeding to chronic pharmacodynamic models. We propose generic tumor-specific correlation-based regression equations to predict tumor-to-plasma ratios (tumor-Kps) in slow- and fast-growing xenograft mouse models.

METHODS

Disposition of 14 basic small molecules was investigated extensively in mouse plasma, tissues and tumors after a single oral dose administration. Linear correlation was assessed and compared between tumor-Kp and normal tissue-to-plasma ratio (tissue-Kps) separately for each tumor xenograft. The developed regression equations were validated by leave-one-out cross-validation (LOOCV) method.

RESULT

Both slow- and fast-growing tumor-Kps showed good correlation (r ² ≥ 0.7) with majority of the normal tissue-Kps. Substantial difference was observed in the slopes of developed equations between two xenografts, which was in line with observed difference in tumor distribution. The linear correlations between tumor-Kp and skin- or spleen-Kp were within the acceptable statistical criteria (LOOCV) across xenografts and the class of compounds evaluated. Since > 70% of tumor-Kps from the test data sets were predicted within a factor of twofold for both slow- and fast-growing xenograft mouse models, the results validate the applicability of the developed equations across xenografts.

CONCLUSION

Tumor-specific correlation-based regression equations were developed and their applicability was adequately validated across xenografts. These equations could be successfully translated to predict tumor concentrations in order to preclude experimental tumor-Kp determination.

Enabling personalized cancer medicine decisions: The challenging pharmacological approach of PBPK models for nanomedicine and pharmacogenomics (Review)

The existing tumor heterogeneity and the complexity of cancer cell biology critically demand powerful translational tools with which to support interdisciplinary efforts aiming to advance personalized cancer medicine decisions in drug development and clinical practice. The development of physiologically based pharmacokinetic (PBPK) models to predict the effects of drugs in the body facilitates the clinical translation of genomic knowledge and the implementation of in vivo pharmacology experience with pharmacogenomics. Such a direction unequivocally empowers our capacity to also make personalized drug dosage scheme decisions for drugs, including molecularly targeted agents and innovative nanoformulations, i.e. in establishing pharmacotyping in prescription. In this way, the applicability of PBPK models to guide individualized cancer therapeutic decisions of broad clinical utility in nanomedicine in real-time and in a cost-affordable manner will be discussed. The latter will be presented by emphasizing the need for combined efforts within the scientific borderlines of genomics with nanotechnology to ensure major benefits and productivity for nanomedicine and personalized medicine interventions.

Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps

INTRODUCTION

Modeling and simulation have become important means of answering questions relevant to the development of a drug, making it possible to assess risks early and to reduce costs. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models contribute to a comprehensive understanding of the drug, covering specific questions from early discovery through lifecycle management stages. As for other disease areas, in oncology, PBPK and PD models are important topics that remain to be addressed.

AREAS COVERED

This review describes current PBPK and PD approaches, their applicability in drug development in general and specifically in the area of oncology. It discusses the current status and then focuses on key challenges and the potential for future use. It provides cases in which modeling currently cannot answer the questions and assesses the requirements to close gaps for PBPK/PD in oncology.

EXPERT OPINION

PBPK/PD models have led to improvements in identifying risks and reducing costs during the drug development process. Nevertheless, there is a lot of potential, where more rigorous integration of biological knowledge and specific experimental design would result in a more comprehensive biological picture. Ideally, such approaches would reveal the extent to which preclinical work can be extrapolated to clinical settings, thus enabling reliable prediction and, ultimately, reducing failed trials in clinical oncology.