Covariate-based dose individualization of the cytotoxic drug indisulam to reduce the risk of severe myelosuppression

Advancing targeted protein degradation for cancer therapy

The human proteome contains approximately 20,000 proteins, and it is estimated that more than 600 of them are functionally important for various types of cancers, including nearly 400 non-enzyme proteins that are challenging to target by traditional occupancy-driven pharmacology. Recent advances in the development of small-molecule degraders, including molecular glues and heterobifunctional degraders such as proteolysis-targeting chimeras (PROTACs), have made it possible to target many proteins that were previously considered undruggable. In particular, PROTACs form a ternary complex with a hijacked E3 ubiquitin ligase and a target protein, leading to polyubiquitination and degradation of the target protein. The broad applicability of this approach is facilitated by the flexibility of individual E3 ligases to recognize different substrates. The vast majority of the approximately 600 human E3 ligases have not been explored, thus presenting enormous opportunities to develop degraders that target oncoproteins with tissue, tumour and subcellular selectivity. In this Review, we first discuss the molecular basis of targeted protein degradation. We then offer a comprehensive account of the most promising degraders in development as cancer therapies to date. Lastly, we provide an overview of opportunities and challenges in this exciting field.

Integrative population pharmacokinetic and pharmacodynamic dose finding approach of the new camptothecin compound namitecan (ST1968)

AIMS

Namitecan is a new camptothecan compound undergoing early clinical development. This study was initiated to build an integrated pharmacokinetic (PK) and pharmacodynamic (PD) population model of namitecan to guide future clinical development.

METHODS

Plasma concentration-time data, neutrophils and thrombocytes were pooled from two phase 1 studies in 90 patients with advanced solid tumours, receiving namitecan as a 2 h infusion on days 1 and 8 every 3 weeks (D1,8) (n = 34), once every 3 weeks (D1) (n = 29) and on 3 consecutive days (D1-3) (n = 27). A linear three compartment PK model was coupled to a semiphysiological PD-model for neutrophils and thrombocytes. Data simulations were used to interrogate various dosing regimens and give dosing recommendations.

RESULTS

Clearance was estimated to be 0.15 l h(-1), with a long terminal half-life of 48 h. Body surface area was not associated with clearance, supporting flat-dosing of namitecan. A significant and clinically relevant association was found between namitecan area under the concentration-time curve (AUC) and the percentage drop of neutrophils (r(2) = 0.51, P < 10(-4)) or thrombocytes (r(2) = 0.49, P < 10(-4)). With a target for haematological dose-limiting toxicity of <20%, the recommended dose was defined as 12.5 mg for the D1,8 regimen, 23 mg for the once every 3 week regimen and 7 mg for the D1-3 regimen.

CONCLUSION

This is the first integrated population PK-PD analysis of the new hydrophilic topoisomerase I inhibitor namitecan, that is currently undergoing early clinical development. A distinct relationship was found between drug exposure and haematological toxicity, supporting flat-dosing once every 3 weeks as the most adequate dosing regimen.

Population pharmacokinetic-pharmacodynamic analysis for eribulin mesilate-associated neutropenia

AIMS

Eribulin mesilate is an inhibitor of microtubule dynamics that is approved for the treatment of late-stage metastatic breast cancer. Neutropenia is one of the major dose-limiting adverse effects of eribulin. The objective of this analysis was to develop a population pharmacokinetic-pharmacodynamic model for eribulin-associated neutropenia.

METHODS

A combined data set of 12 phase I, II and III studies for eribulin mesilate was analysed. The population pharmacokinetics of eribulin was described using a previously developed model. The relationship between eribulin pharmacokinetic and neutropenia was described using a semi-physiological lifespan model for haematological toxicity. Patient characteristics predictive of increased sensitivity to develop neutropenia were evaluated using a simulation framework.

RESULTS

Absolute neutrophil counts were available from 1579 patients. In the final covariate model, the baseline neutrophil count (ANC0) was estimated to be 4.03 × 10(9) neutrophils l(-1) [relative standard error (RSE) 1.2%], with interindividual variability (IIV, 37.3 coefficient of variation % [CV%]). The mean transition time was estimated to be 109 h (RSE 1.8%, IIV 13.9CV%), the feedback constant (γ) was estimated to be 0.216 (RSE 1.4%, IIV 12.2CV%), and the linear drug effect coefficient (SLOPE) was estimated to be 0.0451 μg l(-1) (RSE 3.2%, IIV 54CV%). Albumin, aspartate transaminase and receival of granulocyte colony-stimulating factor (G-CSF) were identified as significant covariates on SLOPE, and albumin, bilirubin, G-CSF, alkaline phosphatase and lactate dehydrogenase were identified as significant covariates on mean transition time.

CONCLUSIONS

The developed model can be applied to investigate optimal treatment strategies quantitatively across different patient groups with respect to neutropenia. Albumin was identified as the most clinically important covariate predictive of interindividual variability in the neutropenia time course.

Mechanism-based pharmacodynamic modeling

Pharmacodynamic modeling is based on a quantitative integration of pharmacokinetics, pharmacological systems, and (patho-) physiological processes for understanding the intensity and time-course of drug effects on the body. Application of such models to the analysis of meaningful experimental data allows for the quantification and prediction of drug-system interactions for both therapeutic and adverse drug responses. In this chapter, commonly used mechanistic pharmacodynamic models are presented with respect to their important features, operable equations, and signature profiles. In addition, literature examples showcasing the utility of these models to adverse drug events are highlighted. Common model types that are covered include simple direct effects, biophase distribution, indirect effects, signal transduction, and irreversible effects.

Prediction of neutropenia-related effects of a new combination therapy with the anticancer drugs BI 2536 (a Plk1 inhibitor) and pemetrexed

This study investigated the feasibility of predicting the neutropenia-related effects of a therapy that combines the investigational drug BI 2536 (inhibitor of Polo-like kinase 1) and pemetrexed, an approved anticancer drug. Predictions were arrived at using the pharmacokinetic/pharmacodynamic (PK/PD) parameters of each of the drugs obtained from monotherapy studies and assuming that the neutropenic effect is additive when the drugs are administered as a combination therapy. Subsequently, a PK/PD model was developed to determine whether this assumption of additive effect was reasonable in relation to these two drugs. All analyses and simulations were performed using the population approach in NONMEM, version VI.