Antitumor Activity of Targeted and Cytotoxic Agents in Murine Subcutaneous Tumor Models Correlates with Clinical Response

Translational PK-PD/TD modeling of antitumor effects and peripheral neuropathy in gemcitabine and nab-paclitaxel chemotherapy from xenograft mice to patients for optimal dose and schedule

PURPOSE

Gemcitabine and nab-paclitaxel (GnP) treatment, the standard first-line chemotherapy for unresectable pancreatic cancer, often causes peripheral neuropathy (PN). To develop alternative dosing strategies to avoid severe PN, understanding the relationship between pharmacokinetics (PK) and pharmacodynamics/toxicodynamics (PD/TD) is necessary. We established a PK-PD/TD model of GnP treatment to develop an optimal dose schedule.

METHODS

A mouse xenograft model of human pancreatic cancer was generated to measure drug concentrations in the plasma and tumor, antitumor effects, and PN after GnP treatment. The Simeoni tumor growth inhibition model with tumor concentrations and empirical indirect response models were used for the PD and TD models, respectively. Clinical outcomes were predicted with reported population estimates of PK parameters in cancer patients.

RESULTS

The PK-PD/TD model simultaneously described the observed tumor volume and paw withdrawal frequency in the von Frey test. For the standard GnP regimen, the model predicted clinical overall response (75.1%), which was overestimated compared to that in a recent phase II study (42.1%) but lower than the observed disease control rate (96.5%). Model simulation showed that dose reduction to less than 40% GnP dose was not effective; a change of dose schedule from every week for 3 weeks to every 2 weeks was a more favorable approach than dose reduction to 60% every week.

CONCLUSION

The PK-PD/TD model-based translational approach provides a guide for optimal dose determination to avoid severe PN while maintaining antitumor effects during GnP chemotherapy. Further research is needed to enhance its applicability and potential for combination chemotherapy regimens.

Preclinical characterization of the absorption and disposition of the brain penetrant PI3K/mTOR inhibitor paxalisib and prediction of its pharmacokinetics and efficacy in human

Glioblastoma multiforme (GBM) is the most common primary brain tumour in adults. Available treatments have not markedly improved patient survival in the last twenty years. However, genomic investigations have showed that the PI3K pathway is frequently altered in this glioma, making it a potential therapeutic target.Paxalisib is a brain penetrant PI3K/mTOR inhibitor (mouse Kp,uu 0.31) specifically developed for the treatment of GBM. We characterised the preclinical pharmacokinetics and efficacy of paxalisib and predicted its pharmacokinetics and efficacious dose in humans.Plasma protein binding of paxalisib was low, with the fraction unbound ranging from 0.25 to 0.43 across species. The hepatic clearance of paxalisib was predicted to be low in mice, rats, dogs and humans, and high in monkeys, from hepatocytes incubations. The plasma clearance was low in mice, moderate in rats and high in dogs and monkeys. Oral bioavailability ranged from 6% in monkeys to 76% in rats.The parameters estimated from the pharmacokinetic/pharmacodynamic modelling of the efficacy in the subcutaneous U87 xenograft model combined with the human pharmacokinetics profile predicted by PBPK modelling suggested that a dose of 56 mg may be efficacious in humans. Paxalisib is currently tested in Phase III clinical trials.

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Growth‐rate model predicts in vivo tumor response from in vitro data

A major challenge in oncology drug development is to elucidate why drugs that show promising results in cancer cell lines in vitro fail in mouse studies or human trials. One of the fundamental steps toward solving this problem is to better predict how in vitro potency translates into in vivo efficacy. A common approach to infer whether a model will respond in vivo is based on in vitro half‐maximal inhibitory concentration values (IC₅₀), but yields limited quantitative comparison between cell lines and drugs, potentially because cell division and death rates differ between cell lines and in vivo models. Other methods based either on mechanistic modeling or machine learning require molecular insights or extensive training data, limiting their use for early drug development. To address these challenges, we propose a mathematical model integrating in vitro growth rate inhibition values with pharmacokinetic parameters to estimate in vivo drug response. Upon calibration with a drug‐specific factor, our model yields precise estimates of tumor growth rate inhibition for in vivo studies based on in vitro data. We then demonstrate how our model can be used to study dosing schedules and perform sensitivity analyses. In addition, it provides meaningful metrics to assess association with genotypes and guide clinical trial design. By relying on commonly collected data, our approach shows great promise for optimizing drug development, better characterizing the efficacy of novel molecules targeting proliferation, and identifying more robust biomarkers of sensitivity while limiting the number of in vivo experiments.

Optimized scaling of translational factors in oncology: from xenografts to RECIST

Purpose

Tumor growth inhibition (TGI) models are regularly used to quantify the PK–PD relationship between drug concentration and in vivo efficacy in oncology. These models are typically calibrated with data from xenograft mice and before being used for clinical predictions, translational methods have to be applied. Currently, such methods are commonly based on replacing model components or scaling of model parameters. However, difficulties remain in how to accurately account for inter-species differences. Therefore, more research must be done before xenograft data can fully be utilized to predict clinical response.

Method

To contribute to this research, we have calibrated TGI models to xenograft data for three drug combinations using the nonlinear mixed effects framework. The models were translated by replacing mice exposure with human exposure and used to make predictions of clinical response. Furthermore, in search of a better way of translating these models, we estimated an optimal way of scaling model parameters given the available clinical data.

Results

The predictions were compared with clinical data and we found that clinical efficacy was overestimated. The estimated optimal scaling factors were similar to a standard allometric scaling exponent of − 0.25.

Conclusions

We believe that given more data, our methodology could contribute to increasing the translational capabilities of TGI models. More specifically, an appropriate translational method could be developed for drugs with the same mechanism of action, which would allow for all preclinical data to be leveraged for new drugs of the same class. This would ensure that fewer clinically inefficacious drugs are tested in clinical trials.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04458-8.

ROADMAPS: An Online Database of Response Data, Dosing Regimens, and Toxicities of Approved Oncology Drugs as Single Agents to Guide Preclinical In Vivo Studies

Preclinical studies provide valuable data in the early development of novel drugs for patients with cancer. Many cancer treatment regimens now utilize multiple agents with different targets to delay the emergence of drug-resistant tumor cells, and experimental agents are often evaluated in combination with FDA-approved drugs. The Biological Testing Branch (BTB) of the U.S. NCI has evaluated more than 70 FDA-approved oncology drugs to date in human xenograft models. Here, we report the first release of a publicly available, downloadable spreadsheet, ROADMAPS (Responses to Oncology Agents and Dosing in Models to Aid Preclinical Studies, dtp.cancer.gov/databases_tools/roadmaps.htm), that provides data filterable by agent, dose, dosing schedule, route of administration, tumor models tested, responses, host mouse strain, maximum weight loss, drug-related deaths, and vehicle formulation for preclinical experiments conducted by the BTB. Data from 70 different single targeted and cytotoxic agents and 140 different xenograft models were included. Multiple xenograft models were tested in immunocompromised mice for many cancer histologies, with lung cancer as the most broadly tested (24 models). Many of the dose levels and schedules used in these experiments were comparable with those tolerated in humans. Targeted and cytotoxic single agents were included. The online spreadsheet will be updated periodically as additional agent/dose/model combinations are evaluated. ROADMAPS is intended to serve as a publicly available resource for the research community to inform the design of clinically relevant, tolerable single and combinatorial regimens in preclinical mouse models.

SIGNIFICANCE

ROADMAPS includes data that can be used to identify tolerable dosing regimens with activity against a variety of human tumors in different mouse strains, providing a resource for planning preclinical studies.

Predicting Approximate Clinically Effective Doses in Oncology Using Preclinical Efficacy and Body Surface Area Conversion: A Retrospective Analysis

The correlation between efficacious doses in human tumor-xenograft mouse models and the human clinical doses of approved oncology agents was assessed using published preclinical data and recommended clinical doses. For 90 approved small molecule anti-cancer drugs, body surface area (BSA) corrected mouse efficacious doses were strongly predictive of human clinical dose ranges with 85.6% of the predictions falling within three-fold (3×) of the recommended clinical doses and 63.3% within 2×. These results suggest that BSA conversion is a useful tool for estimating human doses of small molecule oncology agents from mouse xenograft models from the early discovery stage. However, the BSA based dose conversion poorly predicts for the intravenous antibody and antibody drug conjugate anti-cancer drugs. For antibody-based drugs, five out of 30 (16.7%) predicted doses were within 3× of the recommended clinical dose. The body weight-based dose projection was modestly predictive with 66.7% of drugs predicted within 3× of the recommended clinical dose. The correlation was slightly better in ADCs (77.7% in 3×). The application and limitations of such simple dose estimation methods in the early discovery stage and in the design of clinical trials are also discussed in this retrospective analysis.

Palbociclib-based high-throughput combination drug screening identifies synergistic therapeutic options in HPV-negative head and neck squamous cell carcinoma

BACKGROUND

Deregulation of cell-cycle pathway is ubiquitously observed in human papillomavirus negative (HPV^neg) head and neck squamous cell carcinoma (HNSCC). Despite being an attractive target, CDK4/6 inhibition using palbociclib showed modest or conflicting results as monotherapy or in combination with platinum-based chemotherapy or cetuximab in HPV^neg HNSCC. Thus, innovative agents to augment the efficacy of palbociclib in HPV^neg HNSCC would be welcomed.

METHODS

A collection of 162 FDA-approved and investigational agents was screened in combinatorial matrix format, and top combinations were validated in a broader panel of HPV^neg HNSCC cell lines. Transcriptional profiling was conducted to explore the molecular mechanisms of drug synergy. Finally, the most potent palbociclib-based drug combination was evaluated and compared with palbociclib plus cetuximab or cisplatin in a panel of genetically diverse HPV^neg HNSCC cell lines and patient-derived xenograft models.

RESULTS

Palbociclib displayed limited efficacy in HPV^neg HNSCC as monotherapy. The high-throughput combination drug screening provided a comprehensive palbociclib-based drug-drug interaction dataset, whereas significant synergistic effects were observed when palbociclib was combined with multiple agents, including inhibitors of the PI3K, EGFR, and MEK pathways. PI3K pathway inhibitors significantly reduced cell proliferation and induced cell-cycle arrest in HPV^neg HNSCC cell lines when combined with palbociclib, and alpelisib (a PI3Kα inhibitor) was demonstrated to show the most potent synergy with particularly higher efficacy in HNSCCs bearing PIK3CA alterations. Notably, when compared with cisplatin and cetuximab, alpelisib exerted stronger synergism in a broader panel of cell lines. Mechanistically, RRM2-dependent epithelial mesenchymal transition (EMT) induced by palbociclib, was attenuated by alpelisib and cetuximab rather than cisplatin. Subsequently, PDX models with distinct genetic background further validated that palbociclib plus alpelisib had significant synergistic effects in models harboring PIK3CA amplification.

CONCLUSIONS

This study provides insights into the systematic combinatory effect associated with CDK4/6 inhibition and supports further initiation of clinical trials using the palbociclib plus alpelisib combination in HPV^neg HNSCC with PIK3CA alterations.

Pharmacokinetic and Pharmacodynamic Assessment of Hydroxychloroquine in Breast Cancer

Hydroxychloroquine (HCQ) is being tested in a number of human clinical trials to determine the role of autophagy in response to standard anticancer therapies. However, HCQ pharmacodynamic (PD) responses are difficult to assess in patients, and preclinical studies in mouse models are equivocal with regard to HCQ exposure and inhibition of autophagy. Here, pharmacokinetic (PK) assessment of HCQ in non-tumor-bearing mice after intraperitoneal dosing established 60 mg/kg as the human equivalent dose of HCQ in mice. Autophagy inhibition, cell proliferation, and cell death were assessed in two-dimensional (2D) cell culture and three-dimensional tumor organoids in breast cancer. Mice challenged with breast cancer xenografts were then treated with 60 mg/kg HCQ via intraperitoneal dosing, and subsequent PK and PD responses were assessed. Although autophagic flux was significantly inhibited in cells irrespective of autophagy-dependence status, autophagy-dependent tumors had decreased cell proliferation and increased cell death at earlier time points compared with autophagy-independent tumors. Overall, this study shows that 2D cell culture, three-dimensional tumor organoids, and in vivo studies produce similar results, and in vitro studies can be used as surrogates to recapitulate in vivo antitumor responses of HCQ. SIGNIFICANCE STATEMENT: Autophagy-dependent tumors but not autophagy-independent tumors have decreased cell proliferation and increased cell death after single-agent hydroxychloroquine treatment. However, hydroxychloroquine causes decreased autophagic flux regardless of autophagy status, suggesting its clinical efficacy in the context of autophagy inhibition.

How translational modeling in oncology needs to get the mechanism just right

Translational model‐based approaches have played a role in increasing success in the development of novel anticancer treatments. However, despite this, significant translational uncertainty remains from animal models to patients. Optimization of dose and scheduling (regimen) of drugs to maximize the therapeutic utility (maximize efficacy while avoiding limiting toxicities) is still predominately driven by clinical investigations. Here, we argue that utilizing pragmatic mechanism‐based translational modeling of nonclinical data can further inform this optimization. Consequently, a prototype model is demonstrated that addresses the required fundamental mechanisms.

Harnessing the predictive power of preclinical models for oncology drug development

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

Expanding control of the tumor cell cycle with a CDK2/4/6 inhibitor

The CDK4/6 inhibitor, palbociclib (PAL), significantly improves progression-free survival in HR⁺/HER2^- breast cancer when combined with anti-hormonals. We sought to discover PAL resistance mechanisms in preclinical models and through analysis of clinical transcriptome specimens, which coalesced on induction of MYC oncogene and Cyclin E/CDK2 activity. We propose that targeting the G₁ kinases CDK2, CDK4, and CDK6 with a small-molecule overcomes resistance to CDK4/6 inhibition. We describe the pharmacodynamics and efficacy of PF-06873600 (PF3600), a pyridopyrimidine with potent inhibition of CDK2/4/6 activity and efficacy in multiple in vivo tumor models. Together with the clinical analysis, MYC activity predicts (PF3600) efficacy across multiple cell lineages. Finally, we find that CDK2/4/6 inhibition does not compromise tumor-specific immune checkpoint blockade responses in syngeneic models. We anticipate that (PF3600), currently in phase 1 clinical trials, offers a therapeutic option to cancer patients in whom CDK4/6 inhibition is insufficient to alter disease progression.

Predictive biomarkers for 5-fluorouracil and oxaliplatin-based chemotherapy in gastric cancers via profiling of patient-derived xenografts

Gastric cancer (GC) is commonly treated by chemotherapy using 5-fluorouracil (5-FU) derivatives and platinum combination, but predictive biomarker remains lacking. We develop patient-derived xenografts (PDXs) from 31 GC patients and treat with a combination of 5-FU and oxaliplatin, to determine biomarkers associated with responsiveness. When the PDXs are defined as either responders or non-responders according to tumor volume change after treatment, the responsiveness of PDXs is significantly consistent with the respective clinical outcomes of the patients. An integrative genomic and transcriptomic analysis of PDXs reveals that pathways associated with cell-to-cell and cell-to-extracellular matrix interactions enriched among the non-responders in both cancer cells and the tumor microenvironment (TME). We develop a 30-gene prediction model to determine the responsiveness to 5-FU and oxaliplatin-based chemotherapy and confirm the significant poor survival outcomes among cases classified as non-responder-like in three independent GC cohorts. Our study may inform clinical decision-making when designing treatment strategies.

Poor outcome with anti-programmed death-ligand 1 (PD-L1) antibody due to poor pharmacokinetic properties in PD-1/PD-L1 blockade-sensitive mouse models

Background

Recently, antiprogrammed cell death protein 1 (aPD-1) and antiprogrammed death-ligand 1 (aPD-L1) monoclonal antibodies (mAbs) have been approved. Even though aPD-1 and aPD-L1 mAbs target the same PD-1/PD-L1 axis, it is still unclear whether both mAbs exert equivalent pharmacological activity in patients who are sensitive to PD-1/PD-L1 blockade therapy, as there is no direct comparison of their pharmacokinetics (PK) and antitumor effects. Therefore, we evaluated the differences between both mAbs in PK and therapeutic effects in PD-1/PD-L1 blockade-sensitive mouse models.

Methods

Herein, murine breast MM48 and colon MC38 xenografts were used to analyze the pharmacological activity of aPD-1 and aPD-L1 mAbs. The PK of the mAbs in the tumor-bearing mice was investigated at low and high doses using two radioisotopes (Indium-111 and Iodine-125) to evaluate the accumulation and degradation of the mAbs.

Results

aPD-1 mAb showed antitumor effect in a dose-dependent manner, indicating that the tumor model was sensitive to PD-1/PD-L1 blockade therapy, whereas aPD-L1 mAb failed to suppress tumor growth. The PK study showed that aPD-L1 mAb was accumulated largely in normal organs such as the spleen, liver, and kidney, resulting in low blood concentration and low distributions to tumors at a low dose, even though the tumors expressed PD-L1. Sufficient accumulation of aPD-L1 mAb in tumors was achieved by administration at a high dose owing to the saturation of target-mediated binding in healthy organs. However, degradation of aPD-L1 mAb in tumors was greater than that of aPD-1 mAb, which resulted in poor outcome presumably due to less inhibition of PD-L1 by aPD-L1 mAb than that of PD-1 by aPD-1 mAb.

Conclusion

According to the PK studies, aPD-1 mAb showed linear PK, whereas aPD-L1 mAb showed non-linear PK between low and high doses. Collectively, the poor PK characteristics of aPD-L1 mAb caused lower antitumor activity than of aPD-1 mAb. These results clearly indicated that aPD-L1 mAb required higher doses than aPD-1 mAb in clinical setting. Thus, targeting of PD-1 would be more advantageous than PD-L1 in terms of PK.

A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment

PURPOSE

Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design.

EXPERIMENTAL DESIGN

Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA.

RESULTS

Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported.

CONCLUSIONS

Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.

Current methods in translational cancer research

Recent developments in pre-clinical screening tools, that more reliably predict the clinical effects and adverse events of candidate therapeutic agents, has ushered in a new era of drug development and screening. However, given the rapid pace with which these models have emerged, the individual merits of these translational research tools warrant careful evaluation in order to furnish clinical researchers with appropriate information to conduct pre-clinical screening in an accelerated and rational manner. This review assesses the predictive utility of both well-established and emerging pre-clinical methods in terms of their suitability as a screening platform for treatment response, ability to represent pharmacodynamic and pharmacokinetic drug properties, and lastly debates the translational limitations and benefits of these models. To this end, we will describe the current literature on cell culture, organoids, in vivo mouse models, and in silico computational approaches. Particular focus will be devoted to discussing gaps and unmet needs in the literature as well as current advancements and innovations achieved in the field, such as co-clinical trials and future avenues for refinement.

Exploring a model-based analysis of patient derived xenograft studies in oncology drug development

Purpose

To assess whether a model-based analysis increased statistical power over an analysis of final day volumes and provide insights into more efficient patient derived xenograft (PDX) study designs.

Methods

Tumour xenograft time-series data was extracted from a public PDX drug treatment database. For all 2-arm studies the percent tumour growth inhibition (TGI) at day 14, 21 and 28 was calculated. Treatment effect was analysed using an un-paired, two-tailed t-test (empirical) and a model-based analysis, likelihood ratio-test (LRT). In addition, a simulation study was performed to assess the difference in power between the two data-analysis approaches for PDX or standard cell-line derived xenografts (CDX).

Results

The model-based analysis had greater statistical power than the empirical approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25% whereas the empirical approach required at least 50% TGI. The simulation study confirmed the findings and highlighted that CDX studies require fewer animals than PDX studies which show the equivalent level of TGI.

Conclusions

The study conducted adds to the growing literature which has shown that a model-based analysis of xenograft data improves statistical power over the common empirical approach. The analysis conducted showed that a model-based approach, based on the first mathematical model of tumour growth, was able to detect smaller size of effect compared to the empirical approach which is common of such studies. A model-based analysis should allow studies to reduce animal use and experiment length providing effective insights into compound anti-tumour activity.

Preclinical Metrics Correlate With Drug Activity in Phase II Trials of Targeted Therapies for Non-Small Cell Lung Cancer

Novel oncology drugs often fail to progress from preclinical experiments to FDA approval. Therefore, determining which preclinical or clinical factors associate with drug activity could accelerate development of effective therapies. We investigated whether preclinical metrics and patient characteristics are associated with objective response rate (ORR) in phase II clinical trials of targeted therapies for non-small cell lung cancer (NSCLC). We developed a reproducible process to select a single phase II trial and supporting preclinical publication for a given drug-indication pair, which we defined as the pairing of a small molecule inhibitor (e.g., crizotinib) with the specific patient population for which it was designed to work (e.g., patients with an ALK aberration). We demonstrated that robust drug activity in mice, as measured by change in tumor size, is independently associated with improved ORR in phase II clinical trials. The number of mice utilized in experiments, the number of publications referencing the drug for NSCLC before the phase II clinical trial, and whether the drug was approved for a cancer other than NSCLC also significantly correlated with ORR. Among clinical characteristics, sex, race, histology, and smoking history were significantly associated with ORR. Further research into metrics that correlate with drug activity has the potential to optimize selection of novel therapies for clinical trials and enrich the drug development pipeline, particularly for patients with targetable genetic aberrations and rare cancers.

Rational combination therapy for hepatocellular carcinoma with PARP1 and DNA-PK inhibitors

Understanding differences in DNA double-strand break (DSB) repair between tumor and normal tissues would provide a rationale for developing DNA repair-targeted cancer therapy. Here, using knock-in mouse models for measuring the efficiency of two DSB repair pathways, homologous recombination (HR) and nonhomologous end-joining (NHEJ), we demonstrated that both pathways are up-regulated in hepatocellular carcinoma (HCC) compared with adjacent normal tissues due to altered expression of DNA repair factors, including PARP1 and DNA-PKcs. Surprisingly, inhibiting PARP1 with olaparib abrogated HR repair in HCC. Mechanistically, inhibiting PARP1 suppressed the clearance of nucleosomes at DNA damage sites by blocking the recruitment of ALC1 to DSB sites, thereby inhibiting RPA2 and RAD51 recruitment. Importantly, combining olaparib with NU7441, a DNA-PKcs inhibitor that blocks NHEJ in HCC, synergistically suppressed HCC growth in both mice and HCC patient-derived-xenograft models. Our results suggest the combined inhibition of both HR and NHEJ as a potential therapy for HCC.

Opportunities for Quantitative Translational Modeling in Oncology

A 2-day meeting was held by members of the UK Quantitative Systems Pharmacology Network () in November 2018 on the topic of Translational Challenges in Oncology. Participants from a wide range of backgrounds were invited to discuss current and emerging modeling applications in nonclinical and clinical drug development, and to identify areas for improvement. This resulting perspective explores opportunities for impactful quantitative pharmacology approaches. Four key themes arose from the presentations and discussions that were held, leading to the following recommendations: Evaluate the predictivity and reproducibility of animal cancer models through precompetitive collaboration. Apply mechanism of action (MoA) based mechanistic models derived from nonclinical data to clinical trial data. Apply MoA reflective models across trial data sets to more robustly quantify the natural history of disease and response to differing interventions. Quantify more robustly the dose and concentration dependence of adverse events through mathematical modelling techniques and modified trial design.

Recent advances in physiologically based pharmacokinetic and pharmacodynamic models for anticancer nanomedicines

Nanoparticles (NPs) have distinct pharmacokinetic (PK) properties and can potentially improve the absorption, distribution, metabolism, and elimination (ADME) of small-molecule drugs loaded therein. Owing to the unwanted toxicities of anticancer agents in healthy organs and tissues, their precise delivery to the tumor is an essential requirement. There have been numerous advancements in the development of nanomedicines for cancer therapy. Physiologically based PK (PBPK) models serve as excellent tools for describing and predicting the ADME properties and the efficacy and toxicity of drugs, in combination with pharmacodynamic (PD) models. The recent preliminary application of these modeling approaches to NPs demonstrated their potential benefits in research and development processes relevant to the ADME and pharmacodynamics of NPs and nanomedicines. Here, we comprehensively review the pharmacokinetics of NPs, the developed PBPK models for anticancer NPs, and the developed PD model for anticancer agents.

Pharmacokinetic parameters of ifosfamide in mouse pre-administered with grapefruit juice or naringin

Grapefruit juice (GFJ) and naringin when consumed previously or together with medications may alter their bioavailavility and consequently the clinical effect. Ifosfamide (IF) is an antitumoral agent prescribed against various types of cancer. Nevertheless, there is no information regarding its interaction with the ingestion of GFJ or naringin. The aims of the present report were validating a method for the quantitation of IF in the plasma of mouse, and determine if mice pretreated with GFJ or naringin may modify the IF pharmacokinetics. Our HPLC results to quantify IF showed adequate intra and inter-day precision (RSD < 15%) and accuracy (RE < 15%) indicating reliability. Also, the administration of GFJ or naringin increased C_max of IF 22.9% and 17.8%, respectively, and decreased T_max of IF 19.2 and 53.8%, respectively. The concentration of IF was higher when GFJ (71.35 ± 3.5 µg/mL) was administered with respect to that obtained in the combination naringin with IF (64.12 ± µg/mL); however, the time required to reach such concentration was significantly lower when naringin was administered (p < 0.5). We concluded that pre-administering GFJ and naringin to mice increased the T_max and decreased the C_max of IF.

Pharmacokinetic/Pharmacodynamic Modeling of the Anti-Cancer Effect of Dexamethasone in Pancreatic Cancer Xenografts and Anticipation of Human Efficacious Doses

Dexamethasone (DEX), a synthetic glucocorticoid, exhibited anti-cancer efficacy in pancreatic xenografts derived from patient tumor tissue or cancer cell lines. The aim of this study was to establish pharmacokinetic/pharmacodynamic (PK/PD) models to quantitatively characterize the inhibitory effect of DEX on tumor growth as well as its discrepancy among 3 xenograft models. Data of tumor growth profiles were collected from a patient-derived xenograft (PDX) model in NOD/SCID mice and 2 cell line-derived (PANC-1 and SW1990) xenograft models in BALB/c nude mice. Empirical PK/PD models were developed to establish mathematical relationships between plasma concentration of DEX and tumor growth dynamics after integrating PK parameters extracted from literature. Drug effect in each model was well described by a linear inhibitory function with a potency factor of 4.67, 3.14, and 2.35 L/mg for PDX, PANC-1, and SW1990 xenograft, respectively. Human efficacious doses of DEX were preliminarily predicted through model-based simulation, and 60% tumor growth inhibition at clinical exposure corresponded to a daily dose range of 26-52 mg intravenously. This modeling work quantified the preclinical anti-cancer effect of DEX and demonstrated the feasibility of its medication in pancreatic cancer, which would be conductive to future translational research.

Beyond The T/C Ratio: Old And New Anticancer Activity Scores In Vivo

Assessing the efficacy of anticancer agents in animal models remains a necessary step in the development of new treatment options and plays an important role in their optimization and comparison. Often, however, interpretation of the results is flawed by excessive trust in scores traditionally handed down, but whose origin and limitations have been lost. Here I examine the theories and assumptions underlying the most common rating scales, suggesting improvements to the old scores and proposing the adoption of multi-parameter analysis and interpretation of the results, considering different time-windows. I examined case examples of different scenarios of antiproliferative effects induced by treatment, demonstrating that common scores fail to distinguish between completely different responses to treatment or, in other circumstances, indicate a different outcome when the response is the same. I found that a combination of parameters, including the percent tumor growth between the start and end of treatment, the relative tumor burden at nadir and the absolute growth delay, may distinguish among the different cases and support a correct interpretation of the antitumor response. All these parameters can be derived from individual tumor growth curves in a simple way, without any change to common experimental procedures.

Dose Optimization for Anticancer Drug Combinations: Maximizing Therapeutic Index via Clinical Exposure-Toxicity/Preclinical Exposure-Efficacy Modeling

PURPOSE

Recommended phase II dose (RP2D) determination for combination therapy regimens is a constrained optimization problem of maximizing antitumor activity within the constraint of clinical tolerability to provide a wide therapeutic index. A methodology for addressing this problem was developed and tested using clinical and preclinical data from combinations of the investigational drugs TAK-117, a PI3Kα inhibitor, and TAK-228, a TORC1/2 dual inhibitor.

EXPERIMENTAL DESIGN

Utilizing free fraction-corrected average concentrations, [Formula: see text] and [Formula: see text], which are the primary pharmacokinetic predictors of single-agent preclinical antitumor activity, a preclinical exposure-efficacy surface was characterized, allowing for nonlinear interactions between growth rate inhibition of the agents on a MDA-MB-361 cell line xenograft model. Logistic regression was used to generate an exposure-effect surface for [Formula: see text] and [Formula: see text] versus clinical toxicity outcomes [experiencing a dose-limiting toxicity (DLT)] in single-agent and combination dose-escalation studies. A maximum tolerated exposure curve was defined at which DLT probability was 25%; predicted antitumor activity along this curve was used to determine optimal RP2D.

RESULTS

The toxicity constraint curve determined from early clinical data predicted that any clinically tolerable combination was unlikely to result in greater antitumor activity than either single-agent TAK-117 or TAK-228 administered at their respective MTDs. Similar results were obtained with 10 other cell lines, with one agent or the other predicted to outperform the combination.

CONCLUSIONS

This methodology represents a general, principled way of evaluating and selecting optimal RP2D combinations in oncology. The methodology will be retested upon availability of clinical data from TAK-117/TAK-228 combination phase II studies.See related commentary by Mayawala et al., p. 6564.

Modeling long-term tumor growth and kill after combinations of radiation and radiosensitizing agents

PURPOSE

Radiation therapy, whether given alone or in combination with chemical agents, is one of the cornerstones of oncology. We develop a quantitative model that describes tumor growth during and after treatment with radiation and radiosensitizing agents. The model also describes long-term treatment effects including tumor regrowth and eradication.

METHODS

We challenge the model with data from a xenograft study using a clinically relevant administration schedule and use a mixed-effects approach for model-fitting. We use the calibrated model to predict exposure combinations that result in tumor eradication using Tumor Static Exposure (TSE).

RESULTS

The model is able to adequately describe data from all treatment groups, with the parameter estimates taking biologically reasonable values. Using TSE, we predict the total radiation dose necessary for tumor eradication to be 110 Gy, which is reduced to 80 or 30 Gy with co-administration of 25 or 100 mg kg^-1 of a radiosensitizer. TSE is also explored via a heat map of different growth and shrinkage rates. Finally, we discuss the translational potential of the model and TSE concept to humans.

CONCLUSIONS

The new model is capable of describing different tumor dynamics including tumor eradication and tumor regrowth with different rates, and can be calibrated using data from standard xenograft experiments. TSE and related concepts can be used to predict tumor shrinkage and eradication, and have the potential to guide new experiments and support translations from animals to humans.

Preclinical assessment of the ADME, efficacy and drug-drug interaction potential of a novel NAMPT inhibitor

GNE-617 (N-(4-((3,5-difluorophenyl)sulfonyl)benzyl)imidazo[1,2-a]pyridine-6-carboxamide) is a potent, selective nicotinamide phosphoribosyltransferase (NAMPT) inhibitor being explored as a potential treatment for human cancers. Plasma clearance was low in monkeys and dogs (9.14 mL min^-1kg^-1 and 4.62 mL min^-1kg^-1, respectively) and moderate in mice and rats (36.4 mL min^-1kg^-1 and 19.3 mL min^-1kg^-1, respectively). Oral bioavailability in mice, rats, monkeys and dogs was 29.7, 33.9, 29.4 and 65.2%, respectively. Allometric scaling predicted a low clearance of 3.3 mL min^-1kg^-1 and a volume of distribution of 1.3 L kg^-1 in human. Efficacy (57% tumor growth inhibition) in Colo-205 CRC tumor xenograft mice was observed at an oral dose of 15 mg/kg BID (AUC = 10.4 µM h). Plasma protein binding was moderately high. GNE-617 was stable to moderately stable in vitro. Main human metabolites identified in human hepatocytes were formed primarily by CYP3A4/5. Transporter studies suggested that GNE-617 is likely a substrate for MDR1 but not for BCRP. Simcyp^® simulations suggested a low (CYP2C9 and CYP2C8) or moderate (CYP3A4/5) potential for drug-drug interactions. The potential for autoinhibition was low. Overall, GNE-617 exhibited acceptable preclinical properties and projected human PK and dose estimates.

Survival Prolongation Index as a Novel Metric to Assess Anti-Tumor Activity in Xenograft Models

A single efficacy metric quantifying anti-tumor activity in xenograft models is useful in evaluating different tumors' drug sensitivity and dose-response of an anti-tumor agent. Commonly used metrics include the ratio of tumor volume in treated vs. control mice (T/C), tumor growth inhibition (TGI), ratio of area under the curve (AUC), and growth rate inhibition (GRI). However, these metrics have some limitations. In particular, for biologics with long half-lives, tumor volume (TV) of treated xenografts displays a delay in volume reduction (and in some cases, complete regression) followed by a growth rebound. These observed data cannot be described by exponential functions, which is the underlying assumption of TGI and GRI, and the fit depends on how long the tumor volumes are monitored. On the other hand, T/C and TGI only utilizes information from one chosen time point. Here, we propose a new metric called Survival Prolongation Index (SPI), calculated as the time for drug-treated TV to reach a certain size (e.g., 600 mm³) divided by the time for control TV to reach 600mm³ and therefore not dependent on the chosen final time point t_f. Simulations were conducted under different scenarios (i.e., exponential vs. saturable growth, linear vs. nonlinear kill function). For all cases, SPI is the most linear and growth-rate independent metric. Subsequently, a literature analysis was conducted using 11 drugs to evaluate the correlation between pre-clinically obtained SPI and clinical overall response. This retrospective analysis of approved drugs suggests that a predicted SPI of 2 is necessary for clinical response.

Genome-Informed Targeted Therapy for Osteosarcoma

Osteosarcoma is a highly aggressive cancer for which treatment has remained essentially unchanged for more than 30 years. Osteosarcoma is characterized by widespread and recurrent somatic copy-number alterations (SCNA) and structural rearrangements. In contrast, few recurrent point mutations in protein-coding genes have been identified, suggesting that genes within SCNAs are key oncogenic drivers in this disease. SCNAs and structural rearrangements are highly heterogeneous across osteosarcoma cases, suggesting the need for a genome-informed approach to targeted therapy. To identify patient-specific candidate drivers, we used a simple heuristic based on degree and rank order of copy-number amplification (identified by whole-genome sequencing) and changes in gene expression as identified by RNA sequencing. Using patient-derived tumor xenografts, we demonstrate that targeting of patient-specific SCNAs leads to significant decrease in tumor burden, providing a road map for genome-informed treatment of osteosarcoma. SIGNIFICANCE: Osteosarcoma is treated with a chemotherapy regimen established 30 years ago. Although osteosarcoma is genomically complex, we hypothesized that tumor-specific dependencies could be identified within SCNAs. Using patient-derived tumor xenografts, we found a high degree of response for "genome-matched" therapies, demonstrating the utility of a targeted genome-informed approach.This article is highlighted in the In This Issue feature, p. 1.

The Protein Tyrosine Phosphatase Activity of Eyes Absent Contributes to Tumor Angiogenesis and Tumor Growth

DNA damage repair capacity is required for cells to survive catastrophic DNA damage and proliferate under conditions of intratumoral stress. The ability of the minor histone protein H2AX to serve as a hub for the assembly of a productive DNA damage repair complex is a necessary step in preventing DNA damage-induced cell death. The Eyes Absent (EYA) proteins dephosphorylate the terminal tyrosine residue of H2AX, thus permitting assembly of a productive DNA repair complex. Here, we use genetic and chemical biology approaches to separately query the roles of host vascular endothelial cell and tumor cell EYA in tumor growth. Deletion of Eya3 in host endothelial cells significantly reduced tumor angiogenesis and limited tumor growth in xenografts. Deletion of Eya3 in tumor cells reduced tumor cell proliferation and tumor growth without affecting tumor angiogenesis. A chemical inhibitor of the EYA tyrosine phosphatase activity inhibited both tumor angiogenesis and tumor growth. Simultaneously targeting the tumor vasculature and tumor cells is an attractive therapeutic strategy because it could counter the development of the more aggressive phenotype known to emerge from conventional antiangiogenic agents. Mol Cancer Ther; 17(8); 1659-69. ©2018 AACR.

Systematic Modeling and Design Evaluation of Unperturbed Tumor Dynamics in Xenografts

Xenograft mice are largely used to evaluate the efficacy of oncological drugs during preclinical phases of drug discovery and development. Mathematical models provide a useful tool to quantitatively characterize tumor growth dynamics and also optimize upcoming experiments. To the best of our knowledge, this is the first report where unperturbed growth of a large set of tumor cell lines (n = 28) has been systematically analyzed using a previously proposed model of nonlinear mixed effects (NLME). Exponential growth was identified as the governing mechanism in the majority of the cell lines, with constant rate values ranging from 0.0204 to 0.203 day^-1 No common patterns could be observed across tumor types, highlighting the importance of combining information from different cell lines when evaluating drug activity. Overall, typical model parameters were precisely estimated using designs in which tumor size measurements were taken every 2 days. Moreover, reducing the number of measurements to twice per week, or even once per week for cell lines with low growth rates, showed little impact on parameter precision. However, a sample size of at least 50 mice is needed to accurately characterize parameter variability (i.e., relative S.E. values below 50%). This work illustrates the feasibility of systematically applying NLME models to characterize tumor growth in drug discovery and development, and constitutes a valuable source of data to optimize experimental designs by providing an a priori sampling window and minimizing the number of samples required.

Quantitative translational modeling to facilitate preclinical to clinical efficacy & toxicity translation in oncology

Significant scientific advances in biomedical research have expanded our knowledge of the molecular basis of carcinogenesis, mechanisms of cancer growth, and the importance of the cancer immunity cycle. However, despite scientific advances in the understanding of cancer biology, the success rate of oncology drug development remains the lowest among all therapeutic areas. In this review, some of the key translational drug development objectives in oncology will be outlined. The literature evidence of how mathematical modeling could be used to build a unifying framework to answer these questions will be summarized with recommendations on the strategies for building such a mathematical framework to facilitate the prediction of clinical efficacy and toxicity of investigational antineoplastic agents. Together, the literature evidence suggests that a rigorous and unifying preclinical to clinical translational framework based on mathematical models is extremely valuable for making go/no-go decisions in preclinical development, and for planning early clinical studies.

Combination of COX-2 expression and PIK3CA mutation as prognostic and predictive markers for celecoxib treatment in breast cancer

COX-2 expression level and prognostic value are still a matter of debate in breast cancer (BC). We addressed these points in the context of PIK3CA mutational status. Based on an interesting study of aspirin efficacy in colorectal cancer, we hypothesized that celecoxib antitumoral activity may be restricted to PIK3CA mutated BC.

COX-2 mRNA expression was analyzed in 446 BC samples and in 61 BC patient-derived xenografts (PDX) using quantitative RT-PCR. The prognostic impact of COX-2 expression level was assessed independently and according to PIK3CA mutational status in our cohort and in a validation set of 817 BC. The antitumoral activity of celecoxib was tested in two triple-negative (TN) PDX with a PIK3CA wild-type (wt) or mutated genotype.

COX-2 mRNA was overexpressed in 2% of BC and significantly associated with TN subtype. Metastasis-free survival (MFS) was significantly better in patients with high COX-2 expression level, the prognosis of whom was similar to patients with PIK3CA mutations. TCGA validation cohort confirmed that patients with low COX-2 expression PIK3CA wt tumors had the worse disease-free survival (DFS) compared to all other subgroups. Celecoxib had a significant antitumoral effect in PIK3CA mutated PDX only. Celecoxib antitumoral activity involved S6 ribosomal protein and AKT phosphorylation.

Low expression of COX-2 has a significant negative impact on the MFS/DFS of BC patients. Antitumoral effect of celecoxib is restricted to PIK3CA mutated PDX. These results suggest that PIK3CA mutation may be a new predictive biomarker for celecoxib efficacy.

Characterization and validation of potential therapeutic targets based on the molecular signature of patient-derived xenografts in gastric cancer

Background

Patient-derived xenograft (PDX) models with definite molecular signature are attractive preclinical models for development of novel targeted drugs. Here, we profiled and explored potential therapeutic targets based on characterized PDX models for advanced gastric cancer (AGC).

Methods

The genomic variation and molecular profile of 50 PDX models from AGC patients were analyzed by targeted next-generation sequencing, in situ hybridization, and immunohistochemistry. The antitumor activities of several targeted drugs were investigated in the PDX models. Furthermore, response biomarkers were explored.

Results

Each PDX model had individual histopathological and molecular features, and recurrent alterations in the MAPK, ErbB, VEGF, mTOR, and cell cycle signaling pathways were major events in these PDX models. Several potential drug targets, such as EGFR, MET, and CCNE1, were selected and validated in this study. Volitinib demonstrated strong antitumor activity in PDX models with MET and phosphorylated MET (pMET) overexpression. The EGFR monoclonal antibodies BK011 and cetuximab inhibited tumor growth in a PDX model with EGFR amplification. Afatinib inhibited tumor growth in the PDX models with EGFR amplification, EGFR overexpression, or HER2 amplification. Apatinib was more sensitive in the PDX models with high microvessel density. The CDK1/2/9 inhibitor AZD5438 had superior anti-tumor activity in two models with higher copy number of CCNE1.

Conclusions

PDX models with defined molecular signature are useful for preclinical studies with targeted drugs, and the results should be validated in larger studies with PDX models or in clinical trials.

Electronic supplementary material

The online version of this article (10.1186/s13045-018-0563-y) contains supplementary material, which is available to authorized users.

Upregulation of cell cycle genes in head and neck cancer patients may be antagonized by erufosine's down regulation of cell cycle processes in OSCC cells

The TCGA database was analyzed to identify deregulation of cell cycle genes across 24 cancer types and ensuing effects on patient survival. Pan-cancer analysis showed that head and neck squamous cell carcinoma (HNSCC) ranks amongst the top four cancers showing deregulated cell cycle genes. Also, the median gene expression of all CDKs and cyclins in HNSCC patient samples was higher than that of the global gene expression. This was verified by IHC staining of CCND1 from HNSCC patients. When evaluating the quartiles with highest and lowest expression, increased CCND1/CDK6 levels had negative implication on patient survival. In search for a drug, which may antagonize this tumor profile, the potential of the alkylphosphocholine erufosine was evaluated against cell lines of the HNSCC subtype, oral squamous cell carcinoma (OSCC) using in-vitro and in-vivo assays. Erufosine inhibited growth of OSCC cell lines concentration dependently. Initial microarray findings revealed that cyclins and CDKs were down-regulated concentration dependently upon exposure to erufosine and participated in negative enrichment of cell cycle processes. These findings, indicating a pan-cdk/cyclin inhibition by erufosine, were verified at both, mRNA and protein levels. Erufosine caused a G2/M block and inhibition of colony formation. Significant tumor growth retardation was seen upon treatment with erufosine in a xenograft model. For the decreased cyclin D1 and CDK 4/6 levels found in tumor tissue, these proteins can serve as biomarker for erufosine intervention. The findings demonstrate the potential of erufosine as cell cycle inhibitor in HNSCC treatment, alone or in combination with current therapeutic agents.

PKPD modeling of acquired resistance to anti-cancer drug treatment

Non-small cell lung cancer (NSCLC) patients greatly benefit from the treatment with tyrosine kinase inhibitors (TKIs) targeting the epidermal growth factor receptor (EGFR). However, emergence of acquired resistance inevitable occurs after long-term treatment in most patients and limits clinical improvement. In the present study, resistance to drug treatment in patient-derived NSCLC xenograft mice was assessed and modeling and simulation was applied to understand the dynamics of drug resistance as a basis to explore more beneficial drug regimen. Two semi-mechanistic models were fitted to tumor growth inhibition profiles during and after treatment with erlotinib or gefitinib. The base model proposes that as a result of drug treatment, tumor cells stop proliferating and undergo several stages of damage before they eventually die. The acquired resistance model adds a resistance term to the base model which assumes that resistant cells are emerging from the pool of damaged tumor cells. As a result, tumor cells sensitive to drug treatment will either die or be converted to a drug resistant cell population which is proliferating at a slower growth rate as compared to the sensitive cells. The observed tumor growth profiles were better described by the resistance model and emergence of resistance was concluded. In simulation studies, the selection of resistant cells was explored as well as the time-variant fraction of resistant over sensitive cells. The proposed model provides insight into the dynamic processes of emerging resistance. It predicts tumor regrowth during treatment driven by the selection of resistant cells and explains why faster tumor regrowth may occur after discontinuation of TKI treatment. Finally, it is shown how the semi-mechanistic model can be used to explore different scenarios and to identify optimal treatment schedules in clinical trials.

Human microdosing and mice xenograft data of AGM-130 applied to estimate efficacious doses in patients

PURPOSE

AGM-130 is a cyclin-dependent kinase inhibitor that exhibits dose-dependent efficacy in xenograft mouse models. During preclinical pharmacokinetic (PK) studies, mice and rats showed comparable PK parameters while dogs showed unusually high clearance (CL), which has made human PK prediction challenging. To address this discrepancy, we performed a human microdosing PK and developed a mouse PK/PD model in order to guide the first-in-human studies.

METHODS

A microdose of AGM-130 was given via intravenous injection to healthy subjects. Efficacy data obtained using MCF-7 breast cancer cells implanted in mice was analyzed using pre-existing tumor growth inhibition models. We simulated a human PK/PD profile with the PK parameters obtained from the microdose study and the PD parameters estimated from the xenograft PK/PD model.

RESULTS

The human CL of AGM-130 was 3.08 L/h/kg, which was comparable to CL in mice and rats. The time-courses of tumor growth in xenograft model was well described by a preexisting model. Our simulation indicated that the human doses needed for 50 and 90% inhibition of tumor growth were about 100 and 400 mg, respectively.

CONCLUSIONS

This is the first report of using microdose PK and xenograft PK/PD model to predict efficacious doses before the first-in-human trial in cancer patients. In addition, this work highlights the importance of integration of all of information in PK/PD analysis and illustrates how modeling and simulation can be used to add value in the early stages of drug development.

Current mathematical models for cancer drug discovery

INTRODUCTION

Pharmacometric models represent the most comprehensive approaches for extracting, summarizing and integrating information obtained in the often sparse, limited, and less-than-optimally designed experiments performed in the early phases of oncology drug discovery. Whilst empirical methodologies may be enough for screening and ranking candidate drugs, modeling approaches are needed for optimizing and making economically viable the learn-confirm cycles within an oncology research program and anticipating the dose regimens to be investigated in the subsequent clinical development. Areas covered: Papers appearing in the literature of approximately the last decade reporting modeling approaches applicable to anticancer drug discovery have been listed and commented. Papers were selected based on the interest in the proposed methodology or in its application. Expert opinion: The number of modeling approaches used in the discovery of anticancer drugs is consistently increasing and new models are developed based on the current directions of research of new candidate drugs. These approaches have contributed to a better understanding of new oncological targets and have allowed for the exploitation of the relatively sparse information generated by preclinical experiments. In addition, they are used in translational approaches for guiding and supporting the choice of dosing regimens in early clinical development.

Application of a PK-PD Modeling and Simulation-Based Strategy for Clinical Translation of Antibody-Drug Conjugates: a Case Study with Trastuzumab Emtansine (T-DM1)

Successful clinical translation of antibody-drug conjugates (ADCs) can be challenging due to complex pharmacokinetics and differences between preclinical and clinical tumors. To facilitate this translation, we have developed a general pharmacokinetic-pharmacodynamic (PK-PD) modeling and simulation (M&S)-based strategy for ADCs. Here we present the validation of this strategy using T-DM1 as a case study. A previously developed preclinical tumor disposition model for T-DM1 (Singh and Shah, AAPSJ. 2015; 18(4):861-875) was used to develop a PK-PD model that can characterize in vivo efficacy of T-DM1 in preclinical tumor models. The preclinical data was used to estimate the efficacy parameters for T-DM1. Human PK of T-DM1 was a priori predicted using allometric scaling of monkey PK parameters. The predicted human PK, preclinically estimated efficacy parameters, and clinically observed volume and growth parameters for breast cancer were combined to develop a translated clinical PK-PD model for T-DM1. Clinical trial simulations were performed using the translated PK-PD model to predict progression-free survival (PFS) and objective response rates (ORRs) for T-DM1. The model simulated PFS rates for HER2 1+ and 3+ populations were comparable to the rates observed in three different clinical trials. The model predicted only a modest improvement in ORR with an increase in clinically approved dose of T-DM1. However, the model suggested that a fractionated dosing regimen (e.g., front loading) may provide an improvement in the efficacy. In general, the PK-PD M&S-based strategy presented here is capable of a priori predicting the clinical efficacy of ADCs, and this strategy has been now retrospectively validated for all clinically approved ADCs.

Phase I Dose-Escalation Study of Taselisib, an Oral PI3K Inhibitor, in Patients with Advanced Solid Tumors

Taselisib is a potent and selective tumor growth inhibitor through PI3K pathway suppression. Thirty-four patients with locally advanced or metastatic solid tumors were treated (phase I study, modified 3+3 dose escalation; 5 cohorts; 3-16 mg taselisib once-daily capsule). Taselisib pharmacokinetics were dose-proportional; mean half-life was 40 hours. Frequent dose-dependent, treatment-related adverse events included diarrhea, hyperglycemia, decreased appetite, nausea, rash, stomatitis, and vomiting. At 12 and 16 mg dose levels, dose-limiting toxicities (DLT) were observed, with an accumulation of higher-grade adverse events after the cycle 1 DLT assessment window. Pharmacodynamic findings showed pathway inhibition at ≥3 mg in patient tumor samples, consistent with preclinical PIK3CA-mutant tumor xenograft models. Confirmed response rate was 36% for PIK3CA-mutant tumor patients with measurable disease [5/14: 4 breast cancer (3 patients at 12 mg); 1 non-small cell lung cancer], where responses started at 3 mg, and 0% in patients with tumors without known PIK3CA hotspot mutations (0/15).Significance: Preliminary data consistent with preclinical data indicate increased antitumor activity of taselisib in patients with PIK3CA-mutant tumors (in comparison with patients with tumors without known activating PIK3CA hotspot mutations) starting at the lowest dose tested of 3 mg, thereby supporting higher potency for taselisib against PIK3CA-mutant tumors. Cancer Discov; 7(7); 704-15. ©2017 AACR.See related commentary by Rodon and Tabernero, p. 666This article is highlighted in the In This Issue feature, p. 653.

Translational Model-Based Strategy to Guide the Choice of Clinical Doses for Antibody-Drug Conjugates

This work proposes a model-based approach to help select the phase 1 dosing regimen for the antibody-drug conjugate (ADC) SAR408701 leveraging the available data for 2 other ADCs of the same construct: SAR3419 and SAR566658. First, monkey and human pharmacokinetic (PK) data of SAR566658 and SAR3419 were used to establish the appropriate allometric approach to be applied to SAR408701 monkey PK data. Second, a population pharmacokinetics-pharmacodynamics (PK-PD) model was developed to describe tumor volume evolution following SAR408701 injection in mice. Third, allometric approaches identified for SAR566658 and SAR3419 were applied to SAR408701 monkey PK data to predict the human PK profile. Both SAR566658 and SAR3419 human and monkey PK were best described by a 2-compartment linear model. The relative difference was less than 10% between predicted and observed clearance using allometric exponents of 0.75 and 1, respectively. Tumor volume evolution following SAR408701 injection was best described by a full Simeoni model with a plasma concentration threshold of 4.6 μg/mL for eradication in mice. Both allometric exponents were used to predict SAR408701 PK in human from PK in monkey and to identify the potential effective dosing regimens. This translational strategy may be a valuable tool to design future clinical studies for ADCs, to support selection of the most appropriate dosing regimen, and to estimate the minimal dose required to assure antitumor activity, according to the schedule used.

Targeting and Regulating of an Oncogene via Nanovector Delivery of MicroRNA using Patient-Derived Xenografts

In precision cancer nanomedicine, the key is to identify the oncogenes that are responsible for tumorigenesis, based on which these genetic drivers can be each specifically regulated by a nanovector-directed, oncogene-targeted microRNA (miRNA) for tumor suppression. Fibroblast Growth Factor Receptor 3 (FGFR3) is such an oncogene. The molecular tumor-subtype harboring FGFR3 genomic alteration has been identified via genomic sequencing and referred to as the FGFR3-driven tumors. This genomics-based tumor classification provides further rationale for the development of the FGFR3-targeted miRNA replacement therapy in treating patients with FGFR3 gene abnormity. However, successful miRNA therapy has been hampered by lacking of an efficient delivery vehicle. In this study, a nanovector is developed for microRNA-100 (miR-100) -mediated FGFR3 regulation. The nanovector is composed of the mesoporous magnetic clusters that are conjugated with ternary polymers for efficient miRNA in-vivo delivery. The miRNA-loading capacity of the nanovector is found to be high due to the polycation polymer functionalized mesoporous structure, showing excellent tumor cell transfection and pH-sensitive miRNA release. Delivery of miR-100 to cancer cells effectively down-regulates the expression of FGFR3, inhibits cell proliferation, and induces cell apoptosis in vitro. Patient-derived xenografts (PDXs) are used to evaluate the efficacy of miRNA delivery in the FGFR3-driven tumors. Notably, sharp contrasts are observed between the FGFR3-driven tumors and those without FGFR3 genomic alteration. Only the FGFR3-driven PDXs are significantly inhibited via miR-100 delivery while the non-FGFR3-driven PDXs are not affected, showing promise of precision cancer nanomedicine.