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

Promising Therapeutic Strategies for Hematologic Malignancies: Innovations and Potential

In this review we explore innovative approaches in the treatment of hematologic cancers by combining various therapeutic modalities. We discuss the synergistic potential of combining inhibitors targeting different cellular pathways with immunotherapies, molecular therapies, and hormonal therapies. Examples include combining PI3K inhibitors with proteasome inhibitors, NF-κB inhibitors with immunotherapy checkpoint inhibitors, and neddylation inhibitors with therapies targeting the tumor microenvironment. Additionally, we discuss the potential use of small molecules and peptide inhibitors in hematologic cancer treatment. These multidimensional therapeutic combinations present promising strategies for enhancing treatment efficacy and overcoming resistance mechanisms. However, further clinical research is required to validate their effectiveness and safety profiles in hematologic cancer patients.

Use of Drug Sensitisers to Improve Therapeutic Index in Cancer

The clinical management of malignant tumours is challenging, often leading to severe adverse effects and death. Drug resistance (DR) antagonises the effectiveness of treatments, and increasing drug dosage can worsen the therapeutic index (TI). Current efforts to overcome DR predominantly involve the use of drug combinations, including applying multiple anti-cancerous drugs, employing drug sensitisers, which are chemical agents that enhance pharmacokinetics (PK), including the targeting of cellular pathways and regulating pertinent membrane transporters. While combining multiple compounds may lead to drug-drug interactions (DDI) or polypharmacy effect, the use of drug sensitisers permits rapid attainment of effective treatment dosages at the disease site to prevent early DR and minimise side effects and will reduce the chance of DDI as lower drug doses are required. This review highlights the essential use of TI in evaluating drug dosage for cancer treatment and discusses the lack of a unified standard for TI within the field. Commonly used benefit-risk assessment criteria are summarised, and the critical exploration of the current use of TI in the pharmaceutical industrial sector is included. Specifically, this review leads to the discussion of drug sensitisers to facilitate improved ratios of effective dose to toxic dose directly in humans. The combination of drug and sensitiser molecules might see additional benefits to rekindle those drugs that failed late-stage clinical trials by the removal of detrimental off-target activities through the use of lower drug doses. Drug combinations and employing drug sensitisers are potential means to combat DR. The evolution of drug combinations and polypharmacy on TI are reviewed. Notably, the novel binary weapon approach is introduced as a new opportunity to improve TI. This review emphasises the urgent need for a criterion to systematically evaluate drug safety and efficiency for practical implementation in the field.

The Herbal Combination Shu Gan Jie Yu Regulates the SNCG/ER-a/AKT-ERK Pathway in DMBA-Induced Breast Cancer and Breast Cancer Cell Lines Based on RNA-Seq and IPA Analysis

BACKGROUND

Soothing the liver (called Shu Gan Jie Yu in Chinese, SGJY) is a significant therapeutic method for breast cancer in TCM. In this study, 3 liver-soothing herbs, including Cyperus rotundus L., Citrus medica L. var. sarcodactylis Swingle and Rosa rugosa Thunb. were selected and combined to form a SGJY herbal combinatory.

THE AIM OF THE STUDY

To investigate the inhibiting effect of SGJY on breast cancer in vivo and vitro, and to explore the potential mechanisms.

MATERIALS AND METHODS

SGJY herbal combination was extracted using water. A breast cancer rat model was developed by chemical DMBA by gavage, then treated with SGJY for 11 weeks. The tumor tissue was preserved for RNA sequencing and analyzed by IPA software. The inhibition effects of SGJY on MCF-7 and T47D breast cancer cells were investigated by SRB assay and cell apoptosis analysis, and the protein expression levels of SNCG, ER-α, p-AKT and p-ERK were measured by western blotting.

RESULTS

SGJY significantly reduced the tumor weight and volume, and the level of estradiol in serum. The results of IPA analysis reveal SGJY upregulated 7 canonical pathways and downregulated 16 canonical pathways. Estrogen receptor signaling was the key canonical pathway with 9 genes downregulated. The results of upstream regulator analysis reveal beta-estradiol was the central target; the upstream regulator network scheme showed that 86 genes could affect the expression of the beta-estradiol, including SNCG, CCL21 and MB. Additionally, SGJY was verified to significantly alter the expression of SNCG mRNA, CCL21 mRNA and MB mRNA which was consistent with the data of RNA-Seq. The inhibition effects of SGJY exhibited a dose-dependent response. The apoptosis rates of MCF7 and T47D cells were upregulated. The protein expression of SNCG, ER-α, p-AKT and p-ERK were all significantly decreased by SGJY on MCF-7 and T47D cells.

CONCLUSION

The results demonstrate that SGJY may inhibit the growth of breast cancer. The mechanism might involve downregulating the level of serum estradiol, and suppressing the protein expression in the SNCG/ER-α/AKT-ERK pathway.

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.

Recent Advances of Organ-on-a-Chip in Cancer Modeling Research

Although many studies have focused on oncology and therapeutics in cancer, cancer remains one of the leading causes of death worldwide. Due to the unclear molecular mechanism and complex in vivo microenvironment of tumors, it is challenging to reveal the nature of cancer and develop effective therapeutics. Therefore, the development of new methods to explore the role of heterogeneous TME in individual patients' cancer drug response is urgently needed and critical for the effective therapeutic management of cancer. The organ-on-chip (OoC) platform, which integrates the technology of 3D cell culture, tissue engineering, and microfluidics, is emerging as a new method to simulate the critical structures of the in vivo tumor microenvironment and functional characteristics. It overcomes the failure of traditional 2D/3D cell culture models and preclinical animal models to completely replicate the complex TME of human tumors. As a brand-new technology, OoC is of great significance for the realization of personalized treatment and the development of new drugs. This review discusses the recent advances of OoC in cancer biology studies. It focuses on the design principles of OoC devices and associated applications in cancer modeling. The challenges for the future development of this field are also summarized in this review. This review displays the broad applications of OoC technique and has reference value for oncology development.

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.

Navigating Between Right, Wrong, and Relevant: The Use of Mathematical Modeling in Preclinical Decision Making

The goal of this mini-review is to summarize the collective experience of the authors for how modeling and simulation approaches have been used to inform various decision points from discovery to First-In-Human clinical trials. The article is divided into a high-level overview of the types of problems that are being aided by modeling and simulation approaches, followed by detailed case studies around drug design (Nektar Therapeutics, Genentech), feasibility analysis (Novartis Pharmaceuticals), improvement of preclinical drug design (Pfizer), and preclinical to clinical extrapolation (Merck, Takeda, and Amgen).

Efficient simulation of clinical target response surfaces

Simulation of combination therapies is challenging due to computational complexity. Either a simple model is used to simulate the response for many combinations of concentration to generate a response surface but parameter variability and uncertainty are neglected and the concentrations are constant—the link to the doses to be administered is difficult to make—or a population pharmacokinetic/pharmacodynamic model is used to predict the response to combination therapy in a clinical trial taking into account the time‐varying concentration profile, interindividual variability (IIV), and parameter uncertainty but simulations are limited to only a few selected doses. We devised new algorithms to efficiently search for the combination doses that achieve a predefined efficacy target while taking into account the IIV and parameter uncertainty. The result of this method is a response surface of confidence levels, indicating for all dose combinations the likelihood of reaching the specified efficacy target. We highlight the importance to simulate across a population rather than focus on an individual. Finally, we provide examples of potential applications, such as informing experimental design.

A state-of-the-art review of stratified medicine in cancer: towards a future precision medicine strategy in cancer

BACKGROUND

Building on the success of targeted therapy in certain well-defined cancer genotypes, three platform studies-NCI-MATCH, LUNG-MAP and The National Lung Matrix Trial (NLMT)-have attempted to discover new genotype-matched therapies for people with cancer.

PATIENTS AND METHODS

We review the outputs from these platform studies. This review led us to propose a series of recommendations and considerations that we hope will inform future precision medicine programmes in cancer.

RESULTS

The three studies collectively screened over 13 000 patients. Across 37 genotype-matched cohorts, there have been 66/875 responders, with an overall response rate of 7.5%. Targeting copy number gain yielded 5/199 responses across nine biomarker-drug matched cohorts, with a response rate of 2.5%.

CONCLUSIONS

The majority of these studies used single-agent targeted therapies. Whilst preclinical data can suggest rational combination treatment to reverse adaptive resistance or block parallel activated pathways, there is an essential need for accurate modelling of the toxicity-activity trade-off of combinations. Agent selection is often suboptimal; dose expansion should only be carried out with agents with clear clinical proof of mechanism and high target selectivity. Targeting copy number change has been disappointing; it is crucial to define the drivers on shared amplicons that include the targeted aberration. Maximising outcomes with currently available targeted therapies requires moving towards a more contextualised stratified medicine acknowledging the criticality of the genomic, transcriptional and immunological context on which the targeted aberration is inscribed. Genomic complexity and instability is likely to be a leading cause of targeted therapy failure in genomically complex cancers. Preclinical models must be developed that more accurately capture the genomic complexity of human disease. The degree of attrition of studies carried out after standard-of-care therapy suggests that serious efforts be made to develop a suite of precision medicine studies in the minimal residual disease setting.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease

An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.

Pathogenesis and Clinical Management of Uterine Serous Carcinoma

Uterine serous carcinoma (USC) is an aggressive variant of endometrial cancer that has not been well characterized. It accounts for less than 10% of all endometrial cancers and 80% of endometrial cancer–related deaths. Currently, staging surgery together with chemotherapy or radiotherapy, especially vaginal cuff brachytherapy, is the main treatment strategy for USC. Whole-exome sequencing combined with preclinical and clinical studies are verifying a series of effective and clinically accessible inhibitors targeting frequently altered genes, such as HER2 and PI3K3CA, in varying USC patient populations. Some progress has also been made in the immunotherapy field. The PD-1/PD-L1 pathway has been found to be activated in many USC patients, and clinical trials of PD-1 inhibitors in USC are underway. This review updates the progress of research regarding the molecular pathogenesis and putative clinical management of USC.

Progress and Opportunities to Advance Clinical Cancer Therapeutics Using Tumor Dynamic Models

There is a need for new approaches and endpoints in oncology drug development, particularly with the advent of immunotherapies and the multiple drug combinations under investigation. Tumor dynamics modeling, a key component to oncology "model-informed drug development," has shown a growing number of applications and a broader adoption by drug developers and regulatory agencies in the past years to support drug development and approval in a variety of ways. Tumor dynamics modeling is also being investigated in personalized cancer therapy approaches. These models and applications are reviewed and discussed, as well as the limitations and issues open for further investigations. A close collaboration between stakeholders like clinical investigators, statisticians, and pharmacometricians is warranted to advance clinical cancer therapeutics.

Model Informed Drug Development: Novel Oncology Agents are Lost in Translation

Excitement around and investment in oncology drug development are at unprecedented levels. To maximize the health impact and productivity of this research and development investment, quantitative modeling should impact key decisions in early clinical oncology including Go/No-Go decisions based on early clinical data, and dose selection for late stage studies.See related article by Bottino et al., p. 6633.