Pharmacokinetic-pharmacodynamic guided trial design in oncology

Optimal dynamic regimens with artificial intelligence: The case of temozolomide

We determine an optimal protocol for temozolomide using population variability and dynamic optimization techniques inspired by artificial intelligence. We use a Pharmacokinetics/Pharmacodynamics (PK/PD) model based on Faivre and coauthors (Faivre, et al., 2013) for the pharmacokinetics of temozolomide, as well as the pharmacodynamics of its efficacy. For toxicity, which is measured by the nadir of the normalized absolute neutrophil count, we formalize the myelosuppression effect of temozolomide with the physiological model of Panetta and coauthors (Panetta, et al., 2003). We apply the model to a population with variability as given in Panetta and coauthors (Panetta, et al., 2003). Our optimization algorithm is a variant in the class of Monte-Carlo tree search algorithms. We do not impose periodicity constraint on our solution. We set the objective of tumor size minimization while not allowing more severe toxicity levels than the standard Maximum Tolerated Dose (MTD) regimen. The protocol we propose achieves higher efficacy in the sense that –compared to the usual MTD regimen– it divides the tumor size by approximately 7.66 after 336 days –the 95% confidence interval being [7.36–7.97]. The toxicity is similar to MTD. Overall, our protocol, obtained with a very flexible method, gives significant results for the present case of temozolomide and calls for further research mixing operational research or artificial intelligence and clinical research in oncology.

Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks

Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products. In this paper, a model based on artificial neural network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially increasing pattern of CpG-matured DC to be effective in suppressing the tumor growth.

Model-based drug development applied to oncology

Model-based drug development (MBDD) is an approach that is used to organize the vast and complex data streams that feed the drug development pipelines of small molecule and biotechnology sponsors. Such data streams are ultimately reviewed by the global regulatory community as evidence of a drug's potential to treat and/or harm patients. Some of this information is captured in the scientific literature and prescribing compendiums forming the basis of how new and existing agents will ultimately be administered and further evaluated in the broader patient community. As this data stream evolves, the details of data qualification, the assumptions and/or critical decisions based on these data are lost under conventional drug development paradigms. MBDD relies on the construction of quantitative relationships to connect data from discrete experiments conducted along the drug development pathway. These relationships are then used to ask questions relevant at critical development stages, hopefully, with the understanding that the various scenarios explored represent a path to optimal decision making. Oncology, as a therapeutic area, presents a unique set of challenges and perhaps a different development paradigm as opposed to other disease targets. The poor attrition of development compounds in the recent past attests to these difficulties and provides an incentive for a different approach. In addition, given the reliance on multimodal therapy, oncological disease targets are often treated with both new and older agents spanning several drug classes. As MBDD becomes more integrated into the pharmaceutical research community, a more rational explanation for decisions regarding the development of new oncology agents as well as the proposed treatment regimens that incorporate both new and existing agents can be expected. Hopefully, the end result is a more focussed clinical development programme, which ultimately provides a means to optimize individual patient care.

An integrated disease/pharmacokinetic/pharmacodynamic model suggests improved interleukin-21 regimens validated prospectively for mouse solid cancers

Interleukin (IL)-21 is an attractive antitumor agent with potent immunomodulatory functions. Yet thus far, the cytokine has yielded only partial responses in solid cancer patients, and conditions for beneficial IL-21 immunotherapy remain elusive. The current work aims to identify clinically-relevant IL-21 regimens with enhanced efficacy, based on mathematical modeling of long-term antitumor responses. For this purpose, pharmacokinetic (PK) and pharmacodynamic (PD) data were acquired from a preclinical study applying systemic IL-21 therapy in murine solid cancers. We developed an integrated disease/PK/PD model for the IL-21 anticancer response, and calibrated it using selected “training” data. The accuracy of the model was verified retrospectively under diverse IL-21 treatment settings, by comparing its predictions to independent “validation” data in melanoma and renal cell carcinoma-challenged mice (R²>0.90). Simulations of the verified model surfaced important therapeutic insights: (1) Fractionating the standard daily regimen (50 µg/dose) into a twice daily schedule (25 µg/dose) is advantageous, yielding a significantly lower tumor mass (45% decrease); (2) A low-dose (12 µg/day) regimen exerts a response similar to that obtained under the 50 µg/day treatment, suggestive of an equally efficacious dose with potentially reduced toxicity. Subsequent experiments in melanoma-bearing mice corroborated both of these predictions with high precision (R²>0.89), thus validating the model also prospectively in vivo. Thus, the confirmed PK/PD model rationalizes IL-21 therapy, and pinpoints improved clinically-feasible treatment schedules. Our analysis demonstrates the value of employing mathematical modeling and in silico-guided design of solid tumor immunotherapy in the clinic.

Among the many potential drugs explored within the scope of cancer immunotherapy are selected cytokines which possess promising immune-boosting properties. Yet, the natural involvement of these proteins in multiple, often contradicting biological processes can complicate their use in the clinic. The cytokine interleukin (IL)-21 is no exception: while its strength as an anticancer agent has been established in several animal studies, response rates in melanoma and renal cell carcinoma patients remain low. To help guide the design of effective IL-21 therapy, we have developed a mathematical model that bridges between the complex biology of IL-21 and its optimal clinical use. Our model integrates data from preclinical studies under diverse IL-21 treatment settings, and was validated by extensive experiments in tumor-bearing mice. Model simulations predicted that beneficial, clinically practical IL-21 therapy should be composed of low-dose schedules, and/or schedules in which several partial doses are administered rather than a single complete dose. These findings were subsequently confirmed in mice with melanoma. Thus, future testing of these strategies in solid cancer patients can be a promising starting point for improving IL-21 therapy. Our model can thus provide a computational platform for rationalizing IL-21 regimens and streamlining its clinical development.

Identifying optimal biologic doses of everolimus (RAD001) in patients with cancer based on the modeling of preclinical and clinical pharmacokinetic and pharmacodynamic data

PURPOSE

To use preclinical and clinical pharmacokinetic (PK)/pharmacodynamic (PD) modeling to predict optimal clinical regimens of everolimus, a novel oral mammalian target of rapamycin (mTOR) inhibitor, to carry forward to expanded phase I with tumor biopsy studies in cancer patients.

PATIENTS AND METHODS

Inhibition of S6 kinase 1 (S6K1), a molecular marker of mTOR signaling, was selected for PD analysis in peripheral blood mononuclear cells (PBMCs) in a phase I clinical trial. PK and PD were measured up to 11 days after the fourth weekly dose. A PK/PD model was used to describe the relationship between everolimus concentrations and S6K1 inhibition in PBMCs of cancer patients and in PBMCs and tumors of everolimus-treated CA20948 pancreatic tumor-bearing rats.

RESULTS

Time- and dose-dependent S6K1 inhibition was demonstrated in human PBMCs. In the rat model, a relationship was shown between S6K1 inhibition in tumors or PBMCs and antitumor effect. This allowed development of a direct-link PK/PD model that predicted PBMC S6K1 inhibition-time profiles in patients. Comparison of rat and human profiles simulated by the model suggested that a weekly 20- to 30-mg dose of everolimus would be associated with an antitumor effect in an everolimus-sensitive tumor and that daily administration would exert a greater effect than weekly administration at higher doses.

CONCLUSION

A direct-link PK/PD model predicting the time course of S6K1 inhibition during weekly and daily everolimus administration allowed extrapolation from preclinical studies and first clinical results to select optimal doses and regimens of everolimus to explore in future clinical trials.

Pharmacodynamic model of interleukin-21 effects on red blood cells in cynomolgus monkeys

Interleukin-21 (IL-21) is a novel cytokine that is currently under clinical investigations as a potential anti-cancer agent. Like many other anti-cancer agents, including other interleukins, IL-21 is seen to produce a broad range of biological effects that may be related to both efficacy and safety of treatment. The present analysis investigates the observed pharmacodynamics effects on red blood cells following various treatment schedules of human IL-21 administrated to cynomolgus monkeys. These effects are described by a novel non-linear mixed-effects model that enabled separation of drug effects and sampling effects, the latter believed to be due partly to blood loss and partly to stress induced haemolysis in connection with blood sampling. Two different studies with a total of 9 different treatment groups of cynomolgus monkeys were used for model development. In conclusion, the model describes the IL-21 induced drop in red blood cells to be (1) caused by removal rather than suppression of production, consistent with increased reticulocyte concentration, and (2) considerably delayed compared to dosing, i.e. not related to the drop in red blood cells observed immediately post dose. It is believed that the structural model presented here can be used for other types of drug induced loss of red blood cells, whereas the mechanism for sampling related blood loss is relevant for investigations of anaemia in all pharmacological studies with smaller animals.

Strategies for designing clinical trials for oligonucleotide therapeutics

Recent Phase III clinical trials for oligonucleotide therapeutics have yielded disappointing results. There is growing evidence that trial designs that consider the specific mode of action of these compounds are of crucial importance for their clinical testing. Early trials for oligonucleotide therapeutics should consider additional endpoints for the definition of a biologically active dose rather than focusing on the traditional concept of maximal tolerated dose. In later phases, alternative clinical endpoints and enriching sensitive study populations through innovative trial designs could improve the efficiency of clinical trials for oligonucleotide therapeutics.