Empowering phase II clinical trials to reduce phase III failures

Learning from the LEAP trial series: Optimizing clinical trial design for combination therapies

The LEAP trial series examining the synergy of pembrolizumab and lenvatinib across various cancers has yielded mixed results, highlighting the need for a more detailed understanding of combination therapies. By learning from these trials, we aim to advance the clinical development of more effective combination strategies to improve patient outcomes.

DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles

Evaluation of chemical drug-likeness is essential for the discovery of high-quality drug candidates while avoiding unwarranted biological and clinical trial costs. A high-quality drug candidate should have promising drug-like properties, including pharmacological activity, suitable physicochemical and ADMET properties. Hence, in silico prediction of chemical drug-likeness has been proposed while being a challenging task. Although several prediction models have been developed to assess chemical drug-likeness, they have such drawbacks as sample dependence and poor interpretability. In this study, we developed a novel strategy, named DBPP-Predictor, to predict chemical drug-likeness based on property profile representation by integrating physicochemical and ADMET properties. The results demonstrated that DBPP-Predictor exhibited considerable generalization capability with AUC (area under the curve) values from 0.817 to 0.913 on external validation sets. In terms of application feasibility analysis, the results indicated that DBPP-Predictor not only demonstrated consistent and reasonable scoring performance on different data sets, but also was able to guide structural optimization. Moreover, it offered a new drug-likeness assessment perspective, without significant linear correlation with existing methods. We also developed a free standalone software for users to make drug-likeness prediction and property profile visualization for their compounds of interest. In summary, our DBPP-Predictor provided a valuable tool for the prediction of chemical drug-likeness, helping to identify appropriate drug candidates for further development.

Patient-derived tumor models and their distinctive applications in personalized drug therapy

Tumor models in vitro are conventional methods for developing anti-cancer drugs, evaluating drug delivery, or calculating drug efficacy. However, traditional cell line-derived tumor models are unable to capture the tumor heterogeneity in patients or mimic the interaction between tumors and their surroundings. Recently emerging patient-derived preclinical cancer models, including of patient-derived xenograft (PDX) model, circulating tumor cell (CTC)-derived model, and tumor organoids-on-chips, are promising in personalized drug therapy by recapitulating the complexities and personalities of tumors and surroundings. These patient-derived models have demonstrated potential advantages in satisfying the rigorous demands of specificity, accuracy, and efficiency necessary for personalized drug therapy. However, the selection of suitable models is depending on the specific therapeutic requirements dictated by cancer types, progressions, or the assay scale. As an example, PDX models show remarkable advantages to reconstruct solid tumors in vitro to understand drug delivery and metabolism. Similarly, CTC-derived models provide a sensitive platform for drug testing in advanced-stage patients, while also facilitating the development of drugs aimed at suppressing tumor metastasis. Meanwhile, the demand for large-scale testing has promoted the development of tumor organoids-on-chips, which serves as an optimal tool for high-throughput drug screening. This review summarizes the establishment and development of PDX, CTC-derived models, and tumor organoids-on-chips and addresses their distinctive advantages in drug discovery, sensitive testing, and screening, which demonstrate the potential to aid in the selection of suitable models for fundamental cancer research and clinical trials, and further developing the personalized drug therapy.

Patient-derived tumor models in cancer research: Evaluation of the oncostatic effects of melatonin

The development of new anticancer therapies tends to be very slow. Although their impact on potential candidates is confirmed in preclinical studies, ∼95 % of these new therapies are not approved when tested in clinical trials. One of the main reasons for this is the lack of accurate preclinical models. In this context, there are different patient-derived models, which have emerged as a powerful oncological tool: patient-derived xenografts (PDXs), patient-derived organoids (PDOs), and patient-derived cells (PDCs). Although all these models are widely applied, PDXs, which are created by engraftment of patient tumor tissues into mice, is considered more reliable. In fundamental research, the PDX model is used to evaluate drug-sensitive markers and, in clinical practice, to select a personalized therapeutic strategy. Melatonin is of particular importance in the development of innovative cancer treatments due to its oncostatic impact and lack of adverse effects. However, the literature regarding the oncostatic effect of melatonin in patient-derived tumor models is scant. This review aims to describe the important role of patient-derived models in the development of anticancer treatments, focusing, in particular, on PDX models, as well as their use in cancer research. This review also summarizes the existing literature on the anti-tumoral effect of melatonin in patient-derived models in order to propose future anti-neoplastic clinical applications.

Insulin resistance, kidney outcomes and effects of the endothelin receptor antagonist atrasentan in patients with type 2 diabetes and chronic kidney disease

BACKGROUND

Insulin resistance (IR) is a pathophysiologic hallmark of type 2 diabetes and associated with the presence of chronic kidney disease (CKD). Experimental studies suggest that endothelin-1 increases IR. We assessed the association between IR and cardio-renal outcomes and the effect of the selective endothelin receptor antagonist atrasentan on IR in patients with type 2 diabetes and CKD.

METHODS

We used data from the RADAR and SONAR trials that recruited participants with type 2 diabetes and CKD [eGFR 25-75 mL/min/1.73 m², urine albumin-to-creatinine ratio of 300-5000 mg/g]. IR was calculated using the homeostatic model assessment (HOMA-IR). The association between HOMA-IR and the pre-specified cardio-renal outcomes was assessed using multivariable Cox proportional hazards regression, and effects of atrasentan on HOMA-IR by a linear mixed effect model.

RESULTS

In the SONAR trial, each log-unit increase in HOMA-IR was associated with an increased risk of the composite cardio-renal outcome [hazard ratio 1.32 (95%CI 1.09,1.60; p = 0.004)], kidney outcome [hazard ratio 1.30 (95%CI 1.00,1.68; p-value = 0.048)], and the kidney or all-cause mortality outcome [hazard ratio 1.25 (95%CI 1.01,1.55; p-value = 0.037)]. After 12 weeks treatment in the RADAR trial (N = 123), atrasentan 0.75 mg/day and 1.25 mg/day compared to placebo reduced HOMA-IR by 19.1 (95%CI -17.4, 44.3) and 26.7% (95%CI -6.4, 49.5), respectively. In the SONAR trial (N = 1914), atrasentan 0.75 mg/day compared to placebo reduced HOMA-IR by 9.6% (95%CI 0.6, 17.9).

CONCLUSIONS

More severe IR is associated with increased risk of cardio-renal outcomes. The endothelin receptor antagonist atrasentan reduced IR.

TRIAL REGISTRATION

RADAR trial (Reducing Residual Albuminuria in Subjects With Diabetes and Nephropathy With AtRasentan): NCT01356849. SONAR trial (The Study Of Diabetic Nephropathy With AtRasentan) NCT01858532.

Patient-Derived Xenograft: A More Standard "Avatar" Model in Preclinical Studies of Gastric Cancer

Despite advances in diagnosis and treatment, gastric cancer remains the third most common cause of cancer-related death in humans. The establishment of relevant animal models of gastric cancer is critical for further research. Due to the complexity of the tumor microenvironment and the genetic heterogeneity of gastric cancer, the commonly used preclinical animal models fail to adequately represent clinically relevant models of gastric cancer. However, patient-derived models are able to replicate as much of the original inter-tumoral and intra-tumoral heterogeneity of gastric cancer as possible, reflecting the cellular interactions of the tumor microenvironment. In addition to implanting patient tissues or primary cells into immunodeficient mouse hosts for culture, the advent of alternative hosts such as humanized mouse hosts, zebrafish hosts, and in vitro culture modalities has also facilitated the advancement of gastric cancer research. This review highlights the current status, characteristics, interfering factors, and applications of patient-derived models that have emerged as more valuable preclinical tools for studying the progression and metastasis of gastric cancer.

Treatment de-escalation for HPV+ oropharyngeal cancer: A systematic review and meta-analysis

Human Papillomavirus (HPV) related oropharyngeal carcinoma (OPC) carries a better prognosis compared with HPV-counterparts, thereby pushing the adoption of de-intensification treatment approaches as new strategies to preserve superior oncologic outcomes while minimizing toxicity. We evaluated the effect of treatment de-intensification in terms of overall survival (OS), progression-free survival (PFS), locoregional and distant control (LRC and DM) by selecting prospective or retrospective studies, providing outcome data with reduced intensification versus standard curative treatment in HPV+ OPC patients, with a systematic analysis till September 2020. The primary outcome of interest was OS. Secondary endpoints were PFS, LRC, and DM expressed as HR. A total of 55 studies (from 1393 screened references) were employed for quantitative synthesis for 38 929 patients. Among n = 48 studies with data available, de-intensified treatments reduced OS in HPV+ OPCs (HR = 1.33, 95% CI 1.17-1.52; p < 0.01). In de-escalated treatments, PFS was also decreased (HR = 2.11, 95% CI 1.65-2.69; p < 0.01). Compared with standard treatments, reduced intensity approaches were associated with reduced locoregional and distant disease control (HR = 2.51, 95% CI 1.75-3.59; p < 0.01; and HR = 1.9, 95% CI 1.25-2.9; p < 0.01). Chemoradiation improved survival in a definitive curative setting compared with radiotherapy alone (HR = 1.42, 95% CI 1.16-1.75; p < 0.01). When adjuvant treatments were compared, standard and de-escalation strategies provided similar OS. In conclusion, in patients with HPV+ OPC, de-escalation treatments should not be widely and agnostically adopted in clinical practice, as therein lies a concrete risk of offering a sub-optimal treatment to patients.

Bayesian two-stage design for phase II oncology trials with binary endpoint

In phase II oncology trials, two-stage design allowing early stopping for futility and/or efficacy is frequently used. However, this design based on frequentist statistical approaches could not guarantee a high posterior probability of attending the pre-specified clinically interesting rate from a Bayesian perspective. Here, we proposed a new Bayesian design enabling early terminating for efficacy as well as futility. In addition to the clinically uninteresting and interesting response rate, a prior distribution of response rate, the minimum posterior threshold probabilities and the lengths of the highest posterior density intervals were specified in the design. Finally, we defined the feasible design with the highest total effective predictive probability. We studied the properties of the proposed design and applied it to an oncology trial as an example. The proposed design ensured that the observed response rate fell within prespecified levels of posterior probability. The proposed design provides an alternative design to single-arm two-stage trials.

Do strict decision criteria hamper productivity in the pharmaceutical industry?

The discouragingly high rates of attrition in drug development, and in particular in Phase 2, warrant a closer look at the decision criteria applied for investment in the next phase (Phase 3). We have in this article evaluated Stop/Go criteria after Phase 2, based on a model encompassing both Phase 2 and 3, as well as the eventual outcome on the market. The results indicate that the value of a drug project is often maximized if rather liberal decision criteria are applied. The routine adherence to standard criteria, e.g. requiring significance at 5% level, may lead to an unduly high rate of false negative decisions. This might ultimately hamper the productivity of drug development and leading to potentially useful drugs not being taken forward to benefit the intended patients.

Towards realizing the vision of precision medicine: AI based prediction of clinical drug response

Accurate and individualized prediction of response to therapies is central to precision medicine. However, because of the generally complex and multifaceted nature of clinical drug response, realizing this vision is highly challenging, requiring integrating different data types from the same individual into one prediction model. We used the anti-epileptic drug brivaracetam as a case study and combine a hybrid data/knowledge-driven feature extraction with machine learning to systematically integrate clinical and genetic data from a clinical discovery dataset (n = 235 patients). We constructed a model that successfully predicts clinical drug response [area under the curve (AUC) = 0.76] and show that even with limited sample size, integrating high-dimensional genetics data with clinical data can inform drug response prediction. After further validation on data collected from an independently conducted clinical study (AUC = 0.75), we extensively explore our model to gain insights into the determinants of drug response, and identify various clinical and genetic characteristics predisposing to poor response. Finally, we assess the potential impact of our model on clinical trial design and demonstrate that, by enriching for probable responders, significant reductions in clinical study sizes may be achieved. To our knowledge, our model represents the first retrospectively validated machine learning model linking drug mechanism of action and the genetic, clinical and demographic background in epilepsy patients to clinical drug response. Hence, it provides a blueprint for how machine learning-based multimodal data integration can act as a driver in achieving the goals of precision medicine in fields such as neurology.

Optimal designs for phase II/III drug development programs including methods for discounting of phase II results

Background

Go/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%.

Methods

We integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the “right amount of adjustment”, the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies.

Results

In general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the “naïve” program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design.

Conclusion

Individual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).