Dose Optimization in Oncology Drug Development: The Emerging Role of Pharmacogenomics, Pharmacokinetics, and Pharmacodynamics

REDOMA: Bayesian random-effects dose-optimization meta-analysis using spike-and-slab priors

The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies.

Comprehensive approaches to preclinical evaluation of monoclonal antibodies and their next-generation derivatives

Biotherapeutics hold great promise for the treatment of several diseases and offer innovative possibilities for new treatments that target previously unaddressed medical needs. Despite successful transitions from preclinical to clinical stages and regulatory approval, there are instances where adverse reactions arise, resulting in product withdrawals. As a result, it is essential to conduct thorough evaluations of safety and effectiveness on an individual basis. This article explores current practices, challenges, and future approaches in conducting comprehensive preclinical assessments to ensure the safety and efficacy of biotherapeutics including monoclonal antibodies, toxin-conjugates, bispecific antibodies, single-chain antibodies, Fc-engineered antibodies, antibody mimetics, and siRNA-antibody/peptide conjugates.

Therapeutic drug monitoring in anticancer agents: perspectives of Australian medical oncologists

BACKGROUND

In the development of anticancer agents for solid tumours, body surface area continues to be used to personalise dosing despite minimal evidence for its use over other dosing strategies. With the development of tyrosine kinase inhibitors and other oral targeted anticancer agents, dosing using therapeutic drug monitoring (TDM) is now utilised in many health systems but has had limited uptake in Australia.

AIM

To determine attitudes and barriers to the implementation of TDM among Australian oncologists.

METHODS

A comprehensive questionnaire was developed by the Dutch Pharmacology Oncology Group from semistructured interviews of stakeholders. Seventy-nine questions across seven domains were developed with three free-text responses. This was rationalised to 17 questions with three free-text responses for Australian medical oncologists who identified limited experience with TDM.

RESULTS

Fifty-seven responses were received, with 49 clinicians (86%) identifying limited experience of performing TDM in daily practice. Clinicians were positive (62-91% agree/strongly agree across seven questions) about the advantages of TDM. There was a mixed response for cost-effectiveness and scientific evidence being a barrier to implementation, but strong agreement that prospective studies were needed (75% agreed or strongly agreed); that national treatment guidelines would enable practice (80%) and that a 'pharmacology of oncolytics' education programme would be useful (96%) to provide knowledge for dose individualisation.

CONCLUSION

Despite the limited experience of TDM in oncology in Australia, medical oncologists appear positive about the potential benefit to their patients. We have identified three barriers to implementation that could be targeted for increased adoption of TDM in oncology in Australia.

Simultaneous determination of 11 oral targeted antineoplastic drugs and 2 active metabolites by LC-MS/MS in human plasma and its application to therapeutic drug monitoring in cancer patients

Interindividual exposure differences have been identified in oral targeted antineoplastic drugs (OADs) owing to the pharmacogenetic background of the patients and their susceptibility to multiple factors, resulting in insufficient efficacy or adverse effects. Therapeutic drug monitoring (TDM) can prevent sub-optimal concentrations of OADs and improve their clinical treatment. This study aimed to develop and validate an LC-MS/MS method for the simultaneous quantification of 11 OADs (gefitinib, imatinib, lenvatinib, regorafenib, everolimus, osimertinib, sunitinib, tamoxifen, lapatinib, fruquintinib and sorafenib) and 2 active metabolites (N-desethyl sunitinib and Z-endoxifen) in human plasma. Protein precipitation was used to extract OADs from the plasma samples. Chromatographic separation was performed using an Eclipse XDB-C18 (4.6 × 150 mm, 5 μm) column with a gradient elution of the mobile phase composed of 2 mM ammonium acetate with 0.1 % formic acid in water (solvent A) and methanol (solvent B) at a flow rate of 0.8 mL/min. Mass analysis was performed using positive ion mode electrospray ionization in multiple-reaction monitoring mode. The developed method was validated following FDA bioanalytical guidelines. The calibration curves were linear over the range of 2-400 ng/mL for gefitinib, imatinib, lenvatinib, regorafenib, and everolimus; 1-200 ng/mL for osimertinib, sunitinib, N-desethyl sunitinib, tamoxifen, and Z-endoxifen; and 5-1000 ng/mL for lapatinib, fruquintinib, and sorafenib, with all coefficients of correlation above 0.99. The intra- and inter-day imprecision was below 12.81 %. This method was successfully applied to the routine TDM of gefitinib, lenvatinib, regorafenib, osimertinib, fruquintinib, and sorafenib to optimize the dosage regimens.

Bridging responses to a human telomerase reverse transcriptase-based peptide cancer vaccine candidate in a mechanism-based model

Therapeutic cancer vaccines are novel immuno-therapeutics, aiming to improve clinical outcomes with other immunotherapies. However, obstacles to their successful clinical development remain, which model-informed drug development approaches may address. UV1 is a telomerase based therapeutic cancer vaccine candidate being investigated in phase I clinical trials for multiple indications. We developed a mechanism-based model structure, using a nonlinear mixed-effects modeling techniques, based on longitudinal tumor sizes (sum of the longest diameters, SLD), UV1-specific immunological assessment (stimulation index, SI) and overall survival (OS) data obtained from a UV1 phase I trial including non-small cell lung cancer (NSCLC) patients and a phase I/IIa trial including malignant melanoma (MM) patients. The final structure comprised a mechanistic tumor growth dynamics (TGD) model, a model describing the probability of observing a UV1-specific immune response (SI ≥ 3) and a time-to-event model for OS. The mechanistic TGD model accounted for the interplay between the vaccine peptides, immune system and tumor. The model-predicted UV1-specific effector CD4+ T cells induced tumor shrinkage with half-lives of 103 and 154 days in NSCLC and MM patients, respectively. The probability of observing a UV1-specific immune response was mainly driven by the model-predicted UV1-specific effector and memory CD4+ T cells. A high baseline SLD and a high relative increase from nadir were identified as main predictors for a reduced OS in NSCLC and MM patients, respectively. Our model predictions highlighted that additional maintenance doses, i.e. UV1 administration for longer periods, may result in more sustained tumor size shrinkage.

Natural Chalcones and Derivatives in Colon Cancer: Pre-Clinical Challenges and the Promise of Chalcone-Based Nanoparticles

Colon cancer poses a complex and substantial global health challenge, necessitating innovative therapeutic approaches. Chalcones, a versatile class of compounds with diverse pharmacological properties, have emerged as promising candidates for addressing colon cancer. Their ability to modulate pivotal signaling pathways in the development and progression of colon cancer makes them invaluable as targeted therapeutics. Nevertheless, it is crucial to recognize that although chalcones exhibit promise, further pre-clinical studies are required to validate their efficacy and safety. The journey toward effective colon cancer treatment is multifaceted, involving considerations such as optimizing the sequencing of therapeutic agents, comprehending the resistance mechanisms, and exploring combination therapies incorporating chalcones. Furthermore, the integration of nanoparticle-based drug delivery systems presents a novel avenue for enhancing the effectiveness of chalcones in colon cancer treatment. This review delves into the mechanisms of action of natural chalcones and some derivatives. It highlights the challenges associated with their use in pre-clinical studies, while also underscoring the advantages of employing chalcone-based nanoparticles for the treatment of colon cancer.