Factors associated with failure of oncology drugs in late-stage clinical development: A systematic review

Completion Rate and Positive Results Reporting Among Immunotherapy Trials in Breast Cancer, 2004-2023

Importance

Clinical trials are the path to test and introduce new therapies in the clinic. Trials that are unable to produce results represent inefficiency in the system and may also undermine patient confidence in the new drug development process.

Objectives

To survey the immunotherapy clinical trial landscape of breast cancer between January 2004 and April 2023 and examine what fraction of trials with primary completion date up to November 30, 2022, failed to report outcome, assessing the proportion of trials that yielded positive results and describing trial features associated with these 2 outcomes.

Design, Setting, and Participants

This cross-sectional study included breast cancer immunotherapy trials identified in ClinicalTrials.gov. Trial details and results were retrieved in December 2023. Google Scholar, PubMed, and LARVOL CLIN websites were also searched for reports.

Main Outcomes and Measures

Trial outcome reported as abstract or manuscript. Reported trials were categorized as positive (ie, met its end point) or negative. Association between reporting and trial features were tested using Fisher exact test.

Results

A total of 331 immuno-oncology trials were initiated in breast cancer by April 2023; 242 trials were phase II, 47 were phase I, and 42 phase III. By setting, 212 studies (64.0%) were conducted in metastatic, 94 (28.4%) in neoadjuvant, and 25 (7.6%) in adjuvant settings. Among phase II and III trials, 168 (59.2%) were nonrandomized. One hundred twenty trials had primary completion dates up to November 30, 2022, of which 30 (25.0%; enrolling a combined 2428 patients) failed to report their outcomes; 7 phase I trials (31.8%), 21 phase II trials (23.6%), and 2 phase III trials (22.2%) were unreported. Single-center studies were significantly more likely to be unreported than multicenter studies (19 of 54 [35.2%] vs 9 of 60 [15.0%]; P = .02). Of the 90 reported trials, 47 (52.2%) and 43 (47.8%) were positive and negative, respectively. Seventeen of 19 (89.5%) of the reported randomized trials (accruing a total of 4189 patients) were negative.

Conclusions and Relevance

In this cross-sectional study of immunotherapy breast cancer trials, the large number of trials yielded modest clinical impact. Single-center trials commonly failed to report their outcomes and many phase II studies have not translated into corresponding successful phase III trials.

Poly-pharmacology of existing drugs: How to crack the code?

Drug development in oncology is highly challenging, with less than 5% success rate in clinical trials. This alarming figure points out the need to study in more details the multiple biological effects of drugs in specific contexts. Indeed, the comprehensive assessment of drug poly-pharmacology can provide insights into their therapeutic and adverse effects, to optimize their utilization and maximize the success rate of clinical trials. Recent technological advances have made possible in-depth investigation of drug poly-pharmacology. This review first highlights high-throughput methodologies that have been used to unveil new mechanisms of action of existing drugs. Then, we discuss how emerging chemo-proteomics strategies allow effectively dissecting the poly-pharmacology of drugs in an unsupervised manner.

Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.

Key Pharmacokinetic Parameters of 74 Pediatric Anticancer Drugs Providing Assistance in Preclinical Studies

Novel drug treatments for pediatric patients with cancer are urgently needed. Success of drug development in pediatric oncology has been promising, but many drugs still fail in translation from preclinical to clinical phases. To increase the translational potential, several improvements have been implemented, including the use of clinically achievable concentrations in the drug testing phase. Although pharmacokinetic (PK) parameters of numerous investigated drugs are published, a comprehensive PK overview of the most common drugs in pediatric oncology could guide preclinical trial design and improve the translatability into clinical trials. A review of the literature was conducted for PK parameters of 74 anticancer drugs, from the drug sensitivity profiling library of the INdividualized Therapy FOr Relapsed Malignancies in Childhood (INFORM) registry. PK data in the pediatric population were reported and complemented by adult parameters when no pediatric data were available. In addition, blood-brain barrier (BBB)-penetration assessment of drugs was provided by using the BBB score. Maximum plasma concentration was available for 73 (97%), area under the plasma concentration-time curve for 69 (92%), plasma protein binding for 66 (88%), plasma half-life for 57 (76%), time to maximum concentration for 54 (72%), clearance for 52 (69%), volume of distribution for 37 (49%), lowest plasma concentration reached by the drug before the next dose administration for 21 (28%), and steady-state concentration for 4 (5%) of drugs. Pediatric PK data were available for 48 (65%) drugs. We provide a comprehensive review of PK data for 74 drugs studied in pediatric oncology. This data set can serve as a reference to design experiments more closely mimicking drug PK conditions in patients, and may thereby increase the probability of successful clinical translation.

Improved inference for MCP-Mod approach using time-to-event endpoints with small sample sizes

The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework has been recently approved by the U.S. Food, Administration, and European Medicines Agency as fit-for-purpose for phase II studies. Nonetheless, this approach relies on the asymptotic properties of Maximum Likelihood (ML) estimators, which might not be reasonable for small sample sizes. In this paper, we derived improved ML estimators and correction for their covariance matrices in the censored Weibull regression model based on the corrective and preventive approaches. We performed two simulation studies to evaluate ML and improved ML estimators with their covariance matrices in (i) a regression framework (ii) the Multiple Comparison Procedures with Modeling Techniques framework. We have shown that improved ML estimators are less biased than ML estimators yielding Wald-type statistics that controls type I error without loss of power in both frameworks. Therefore, we recommend the use of improved ML estimators in the MCP-Mod approach to control type I error at nominal value for sample sizes ranging from 5 to 25 subjects per dose.

A single-arm study design with non-inferiority and superiority time-to-event endpoints: a tool for proof-of-concept and de-intensification strategies in breast cancer

De-escalation trials in oncology evaluate therapies that aim to improve the quality of life of patients with low-risk cancer by avoiding overtreatment. Non-inferiority randomized trials are commonly used to investigate de-intensified regimens with similar efficacy to that of standard regimens but with fewer adverse effects (ESMO evidence tier A). In cases where it is not feasible to recruit the number of patients needed for a randomized trial, single-arm prospective studies with a hypothesis of non-inferiority can be conducted as an alternative. Single-arm studies are also commonly used to evaluate novel treatment strategies (ESMO evidence tier B). A single-arm design that includes both non-inferiority and superiority primary objectives will enable the ranking of clinical activity and other parameters such as safety, pharmacokinetics, and pharmacodynamics data. Here, we describe the statistical principles and procedures to support such a strategy. The non-inferiority margin is calculated using the fixed margin method. Sample size and statistical analyses are based on the maximum likelihood method for exponential distributions. We present example analyses in metastatic and adjuvant settings to illustrate the usefulness of our methodology. We also explain its implementation with nonparametric methods. Single-arm designs with non-inferiority and superiority analyses are optimal for proof-of-concept and de-escalation studies in oncology.

Delivering the precision oncology paradigm: reduced R&D costs and greater return on investment through a companion diagnostic informed precision oncology medicines approach

BACKGROUND

Precision oncology medicines represent a paradigm shift compared to non-precision oncology medicines in cancer therapy, in some situations delivering more clinical benefit, and potentially lowering healthcare costs. We determined whether employing a companion diagnostic (CDx) approach during oncology medicines development delivers effective therapies that are within the cost constraints of current health systems. R&D costs of developing a medicine are subject to debate, with average estimates ranging from $765 million (m) to $4.6 billion (b). Our aim was to determine whether precision oncology medicines are cheaper to bring from R&D to market; a secondary goal was to determine whether precision oncology medicines have a greater return on investment (ROI).

METHOD

Data on oncology medicines approved between 1997 and 2020 by the US Food and Drug Administration (FDA) were analysed from the Securities and Exchange Commission (SEC) filings. Data were compiled from 10-K, 10-Q, and 20-F financial performance filings on medicines' development costs through their R&D lifetime. Clinical trial data were split into clinical trial phases 1-3 and probability of success (POS) of trials was calculated, along with preclinical costs. Cost-of-capital (CoC) approach was applied and, if appropriate, a tax rebate was subtracted from the total.

RESULTS

Data on 42 precision and 29 non-precision oncology medicines from 56 companies listed by the National Cancer Institute which had complete data available were analysed. Estimated mean cost to deliver a new oncology medicine was $4.4b (95% CI, $3.6-5.2b). Costs to bring a precision oncology medicine to market were $1.1b less ($3.5b; 95% CI, $2.7-4.5b) compared to non-precision oncology medicines ($4.6b; 95% CI, $3.5-6.1b). The key driver of costs was POS of clinical trials, accounting for a difference of $591.3 m. Additional data analysis illustrated that there was a 27% increase in return on investment (ROI) of precision oncology medicines over non-precision oncology medicines.

CONCLUSION

Our results provide an accurate estimate of the R&D spend required to bring an oncology medicine to market. Deployment of a CDx at the earliest stage substantially lowers the cost associated with oncology medicines development, potentially making them available to more patients, while staying within the cost constraints of cancer health systems.

Bypassing phase 2 in cancer drug development erodes the risk/benefit balance in phase 3 trials

OBJECTIVES

Drug developers sometimes launch phase 3 (P3) trials without supporting evidence from phase 2 (P2) trials. We call this practice "P2 bypass." The aims of this study were to estimate the prevalence of P2 bypass and to compare the safety and efficacy results for P3 trials that bypassed with those that did not.

STUDY DESIGN AND SETTING

We created a sample of P3 solid tumor trials registered on ClinicalTrials.gov with primary completion dates between 2013 and 2019. We then attempted to match each with a supporting P2 trial using strict and broad criteria. P3 outcomes were meta-analyzed using a random effects model with subgroup contrast between trials that bypassed and those that did not.

RESULTS

129 P3 trial arms met eligibility and nearly half involved P2 bypass. P3 trials involving P2 bypass produced significantly and nonsignificantly worse pooled efficacy estimates using broad and strict matching criteria, respectively. We did not observe significant differences in safety outcomes between P3 trials that bypassed P2 and those that did not.

CONCLUSION

The risk/benefit balance of P3 trials that bypassed P2 is less favourable than for trials supported by P2.

Intravital imaging to study cancer progression and metastasis

Navigation through the bulk tumour, entry into the blood vasculature, survival in the circulation, exit at distant sites and resumption of proliferation are all steps necessary for tumour cells to successfully metastasize. The ability of tumour cells to complete these steps is highly dependent on the timing and sequence of the interactions that these cells have with the tumour microenvironment (TME), including stromal cells, the extracellular matrix and soluble factors. The TME thus plays a major role in determining the overall metastatic phenotype of tumours. The complexity and cause-and-effect dynamics of the TME cannot currently be recapitulated in vitro or inferred from studies of fixed tissue, and are best studied in vivo, in real time and at single-cell resolution. Intravital imaging (IVI) offers these capabilities, and recent years have been a time of immense growth and innovation in the field. Here we review some of the recent advances in IVI of mammalian models of cancer and describe how IVI is being used to understand cancer progression and metastasis, and to develop novel treatments and therapies. We describe new techniques that allow access to a range of tissue and cancer types, novel fluorescent reporters and biosensors that allow fate mapping and the probing of functional and phenotypic states, and the clinical applications that have arisen from applying these techniques, reporters and biosensors to study cancer. We finish by presenting some of the challenges that remain in the field, how to address them and future perspectives.

Machine learning-based prediction of drug approvals using molecular, physicochemical, clinical trial, and patent-related features

BACKGROUND

Drug development productivity has been declining lately due to elevated costs and reduced discovery rates. Therefore, pharmaceutical companies have been seeking alternative ways to determine and evaluate drug candidates.

RESEARCH DESIGN AND METHODS

In this work, we proposed a new computational approach to directly predict the regulatory approval of drug candidates, and implemented it as a method called 'DrugApp.' To accomplish this task, we employed multiple types of features including molecular and physicochemical properties of drug candidates, together with clinical trial and patent-related features, which are then processed by random forest classifiers to train our disease group-specific approval prediction models.

RESULTS

Our evaluations indicated DrugApp has a high and robust prediction performance. Within a use-case study, we showed our method can predict phase IV trial drugs that are later withdrawn from the market due to severe side effects. Finally, we used DrugApp models to forecast the approval of drug candidates that are currently in phases I/II/III of clinical trials.

CONCLUSIONS

We hope that our study will aid the research community in terms of evaluating and improving the process of drug development. The datasets, source code, results, and pre-trained models of DrugApp are freely available at https://github.com/HUBioDataLab/DrugApp.

Do Early Phase Oncology Trials Predict Clinical Efficacy in Subsequent Biomarker-Enriched Phase III Randomized Trials?

BACKGROUND

Promising early phase trial results of biomarker-targeted therapies have occasionally led to regulatory approval.

OBJECTIVE

We examined if early phase trials were predictive of efficacy in randomized controlled trials (RCTs) with matching treatment settings.

PATIENTS AND METHODS

Cancer drug RCTs conducted between January 2006 and March 2021 were identified through Clinicaltrials.gov. Biomarker-enriched RCTs and associated matching early phase trials were included. Trial pairs were compared using objective response rate (ORR) and progression-free survival (PFS). We examined whether early phase trials results were associated with RCT results using logistic regression.

RESULTS

The search yielded 2157 unique RCTs and 27 RCTs pairing with early phase trials were included. Based on average difference of trial pairs, ORR was similar (1.6%; 95% confidence interval (CI) - 2.5 to 5.6, p = 0.50) and median PFS was higher in early phase trials (2.0 months; 95% CI 0.9-3.0, p < 0.05). On an individual pair basis, there was large variability in difference for ORR (range - 23.9 to 20.2%) and median PFS (range - 0.8 to 7.4 months). The probability of the RCT meeting its primary endpoint is 95% (95% prediction interval (PI) 72.8-99.3%) when the early phase trial ORR is 77.7%.

CONCLUSIONS

Overall, in early phase trials, ORR has minimal bias and median PFS appears to be slightly overestimated. Substantial variability between trials suggests early phase trial results may be inconsistent with subsequent RCT. Early phase trial results may be associated with RCTs meeting their primary endpoint when ORR is very high; however, caution must be exercised when using early phase trials as representative of RCTs.

Maximizing the value of phase III trials in immuno-oncology: A checklist from the Society for Immunotherapy of Cancer (SITC)

The broad activity of agents blocking the programmed cell death protein 1 and its ligand (the PD-(L)1 axis) revolutionized oncology, offering long-term benefit to patients and even curative responses for tumors that were once associated with dismal prognosis. However, only a minority of patients experience durable clinical benefit with immune checkpoint inhibitor monotherapy in most disease settings. Spurred by preclinical and correlative studies to understand mechanisms of non-response to the PD-(L)1 antagonists and by combination studies in animal tumor models, many drug development programs were designed to combine anti-PD-(L)1 with a variety of approved and investigational chemotherapies, tumor-targeted therapies, antiangiogenic therapies, and other immunotherapies. Several immunotherapy combinations improved survival outcomes in a variety of indications including melanoma, lung, kidney, and liver cancer, among others. This immunotherapy renaissance, however, has led to many combinations being advanced to late-stage development without definitive predictive biomarkers, limited phase I and phase II data, or clinical trial designs that are not optimized for demonstrating the unique attributes of immune-related antitumor activity-for example, landmark progression-free survival and overall survival. The decision to activate a study at an individual site is investigator-driven, and generalized frameworks to evaluate the potential for phase III trials in immuno-oncology to yield positive data, particularly to increase the number of curative responses or otherwise advance the field have thus far been lacking. To assist in evaluating the potential value to patients and the immunotherapy field of phase III trials, the Society for Immunotherapy of Cancer (SITC) has developed a checklist for investigators, described in this manuscript. Although the checklist focuses on anti-PD-(L)1-based combinations, it may be applied to any regimen in which immune modulation is an important component of the antitumor effect.

Shining a Light on Cancer - Photonics in Microfluidic Tumor Modelling and Biosensing

Microfluidic platforms represent a powerful approach to miniaturizing important characteristics of cancers, improving in vitro testing by increasing physiological relevance. Different tools can manipulate cells and materials at the microscale, but few offer the efficiency and versatility of light and optical technologies. Moreover, light-driven technologies englobe a broad toolbox for quantifying critical biological phenomena. Herein, we review the role of photonics in microfluidic 3D cancer modeling and biosensing from three major perspectives. First, we look at optical-driven technologies that allow biomaterials and living cells to be manipulated with micro-sized precision and the opportunities to advance 3D microfluidic models by engineering cancer microenvironments' hallmarks, such as their architecture, cellular complexity, and vascularization. Second, we delve into the growing field of optofluidics, exploring how optical tools can directly interface microfluidic chips, enabling the extraction of relevant biological data, from single fluorescent signals to the complete 3D imaging of diseased cells within microchannels. Third, we review advances in optical cancer biosensing, focusing on how light-matter interactions can detect biomarkers, rare circulating tumor cells, and cell-derived structures such as exosomes. We overview photonic technologies' current challenges and caveats in microfluidic 3D cancer models, outlining future research avenues that may catapult the field. This article is protected by copyright. All rights reserved.

Drosophila melanogaster: A platform for anticancer drug discovery and personalized therapies

Cancer is a complex disease whereby multiple genetic aberrations, epigenetic modifications, metabolic reprogramming, and the microenvironment contribute to the development of a tumor. In the traditional anticancer drug discovery pipeline, drug candidates are usually screened in vitro using two-dimensional or three-dimensional cell culture. However, these methods fail to accurately mimic the human disease state. This has led to the poor success rate of anticancer drugs in the preclinical stages since many drugs are abandoned due to inefficacy or toxicity when transitioned to whole-organism models. The common fruit fly, Drosophila melanogaster, has emerged as a beneficial system for modeling human cancers. Decades of fundamental research have shown the evolutionary conservation of key genes and signaling pathways between flies and humans. Moreover, Drosophila has a lower genetic redundancy in comparison to mammals. These factors, in addition to the advancement of genetic toolkits for manipulating gene expression, allow for the generation of complex Drosophila genotypes and phenotypes. Numerous studies have successfully created Drosophila models for colorectal, lung, thyroid, and brain cancers. These models were utilized in the high-throughput screening of FDA-approved drugs which led to the identification of several compounds capable of reducing proliferation and rescuing phenotypes. More noteworthy, Drosophila has also unlocked the potential for personalized therapies. Drosophila ‘avatars’ presenting the same mutations as a patient are used to screen multiple therapeutic agents targeting multiple pathways to find the most appropriate combination of drugs. The outcomes of these studies have translated to significant responses in patients with adenoid cystic carcinoma and metastatic colorectal cancers. Despite not being widely utilized, the concept of in vivo screening of drugs in Drosophila is making significant contributions to the current drug discovery pipeline. In this review, we discuss the application of Drosophila as a platform in anticancer drug discovery; with special focus on the cancer models that have been generated, drug libraries that have been screened and the status of personalized therapies. In addition, we elaborate on the biological and technical limitations of this system.

A multi-organ-on-chip to recapitulate the infiltration and the cytotoxic activity of circulating NK cells in 3D matrix-based tumor model

The success of immunotherapeutic approaches strictly depends on the immune cells interaction with cancer cells. While conventional in vitro cell cultures under-represent the complexity and dynamic crosstalk of the tumor microenvironment, animal models do not allow deciphering the anti-tumor activity of the human immune system. Therefore, the development of reliable and predictive preclinical models has become crucial for the screening of immune-therapeutic approaches. We here present an organ-on-chip organ on chips (OOC)-based approach for recapitulating the immune cell Natural Killer (NK) migration under physiological fluid flow, infiltration within a 3D tumor matrix, and activation against neuroblastoma cancer cells in a humanized, fluid-dynamic environment. Circulating NK cells actively initiate a spontaneous “extravasation” process toward the physically separated tumor niche, retaining their ability to interact with matrix-embedded tumor cells, and to display a cytotoxic effect (tumor cell apoptosis). Since NK cells infiltration and phenotype is correlated with prognosis and response to immunotherapy, their phenotype is also investigated: most importantly, a clear decrease in CD16-positive NK cells within the migrated and infiltrated population is observed. The proposed immune-tumor OOC-based model represents a promising approach for faithfully recapitulating the human pathology and efficiently employing the immunotherapies testing, eventually in a personalized perspective. An immune-organ on chip to recapitulate the tumor-mediated infiltration of circulating immune cells within 3D tumor model.

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology

Background. Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced melanomas, but resistance always occurs. This study is aimed at identifying the potential target genes and candidate drugs for drug-resistant melanoma effectively with computational methods. Methods. Identification of genes associated with drug-resistant melanomas was conducted using the text mining tool pubmed2ensembl. Further gene screening was carried out by GO and KEGG pathway enrichment analyses. The PPI network was constructed using STRING database and Cytoscape. GEPIA was used to perform the survival analysis and conduct the Kaplan-Meier curve. Drugs targeted at these genes were selected in Pharmaprojects. The binding affinity scores of drug-target interactions were predicted by DeepPurpose. Results. A total of 433 genes were found associated with drug-resistant melanomas by text mining. The most statistically differential functional enriched pathways of GO and KEGG analyses contained 348 genes, and 27 hub genes were further screened out by MCODE in Cytoscape. Six genes were identified with statistical differences after survival analysis and literature review. 16 candidate drugs targeted at hub genes were found by Pharmaprojects under our restrictions. Finally, 11 ERBB2-targeted drugs with top affinity scores were predicted by DeepPurpose, including 10 ERBB2 kinase inhibitors and 1 antibody-drug conjugate. Conclusion. Text mining and bioinformatics are valuable methods for gene identification in drug discovery. DeepPurpose is an efficient and operative deep learning tool for predicting the DTI and selecting the candidate drugs.

Metallodrugs in cancer nanomedicine

Metal complexes are extensively used for cancer therapy. The multiple variables available for tuning (metal, ligand, and metal-ligand interaction) offer unique opportunities for drug design, and have led to a vast portfolio of metallodrugs that can display a higher diversity of functions and mechanisms of action with respect to pure organic structures. Clinically approved metallodrugs, such as cisplatin, carboplatin and oxaliplatin, are used to treat many types of cancer and play prominent roles in combination regimens, including with immunotherapy. However, metallodrugs generally suffer from poor pharmacokinetics, low levels of target site accumulation, metal-mediated off-target reactivity and development of drug resistance, which can all limit their efficacy and clinical translation. Nanomedicine has arisen as a powerful tool to help overcome these shortcomings. Several nanoformulations have already significantly improved the efficacy and reduced the toxicity of (chemo-)therapeutic drugs, including some promising metallodrug-containing nanomedicines currently in clinical trials. In this critical review, we analyse the opportunities and clinical challenges of metallodrugs, and we assess the advantages and limitations of metallodrug delivery, both from a nanocarrier and from a metal-nano interaction perspective. We describe the latest and most relevant nanomedicine formulations developed for metal complexes, and we discuss how the rational combination of coordination chemistry with nanomedicine technology can assist in promoting the clinical translation of metallodrugs.

Generation of reproductive transgenic pigs of a CRISPR-Cas9-based oncogene-inducible system by somatic cell nuclear transfer

Alternative cancer models that are close to humans are required to create more valuable preclinical results during oncology studies. Here, we developed a new onco-pig model via developing a CRISPR-Cas9-based Conditional Polycistronic gene expression Cassette (CRI-CPC) system to control the tumor inducing simian virus 40 large T antigen (SV40LT) and oncogenic HRASG12V. After conducting somatic cell nuclear transfer (SCNT), transgenic embryos were transplanted into surrogate mothers and five male piglets were born. Umbilical cord analysis confirmed that all piglets were transgenic. Two of them survived, and they expressed a detectable green fluorescence. We tested whether our CRI-CPC models were naturally fertile and whether the CRI-CPC system was stably transferred to the offspring. By mating with a normal female pig, four offspring piglets were successfully produced. Among them, only three male piglets were transgenic. Finally, we tested their applicability as cancer models after transduction of Cas9 into fibroblasts from each CRI-CPC pig in vitro, resulting in cell acquisition of cancerous characteristics via the induction of oncogene expression. These results showed that our new CRISPR-Cas9-based onco-pig model was successfully developed. This article is protected by copyright. All rights reserved.

Psychosocial Profile of Juvenile and Adult Offenders Who Acknowledge Having Committed Child-to-Parent Violence

The main objective of this study was to establish the psychosocial profile of adolescents and adults who have admitted to committing child-to-parent violence (CPV) and were serving a judicial sanction or prison sentence, respectively. Two groups of participants took part in this study. The first group was made up of 89 male youths who were serving judicial sanctions, and the second group was made up of 70 men serving a prison sentence. A cross-sectional retrospective design with concurrent measurements was used in this study. Group differences in the exposure-to-violence variables were conducted. Automatic regression models were used to estimate a self-reported CPV. In relation to the variables of indirect exposure to violence, statistically significant differences between those who admitted having committed CPV and those who did not, irrespective of being adults or adolescents, were found for seeing violence in class and at home but not for seeing violence on the street or on television. Regarding the variables related to experiencing violence, the results showed statistically significant differences in experiencing violence at home but not in class or on the street. The best predictive model of CPV includes some of the dimensions of self-concept, specifically academic and family self-concept, as well as the avoidant and rational problem-solving styles and the negative orientation toward problems. The results have shown the existence of a CPV offender profile that is common to minors and adults.

Microphysiological Systems: Stakeholder Challenges to Adoption in Drug Development

Microphysiological systems (MPS) or tissue chips/organs-on-chips are novel in vitro models that emulate human physiology at the most basic functional level. In this review, we discuss various hurdles to widespread adoption of MPS technology focusing on issues from multiple stakeholder sectors, e.g., academic MPS developers, commercial suppliers of platforms, the pharmaceutical and biotechnology industries, and regulatory organizations. Broad adoption of MPS technology has thus far been limited by a gap in translation between platform developers, end-users, regulatory agencies, and the pharmaceutical industry. In this brief review, we offer a perspective on the existing barriers and how end-users may help surmount these obstacles to achieve broader adoption of MPS technology.

3D Cell Culture Models as Recapitulators of the Tumor Microenvironment for the Screening of Anti-Cancer Drugs

Today, innovative three-dimensional (3D) cell culture models have been proposed as viable and biomimetic alternatives for initial drug screening, allowing the improvement of the efficiency of drug development. These models are gaining popularity, given their ability to reproduce key aspects of the tumor microenvironment, concerning the 3D tumor architecture as well as the interactions of tumor cells with the extracellular matrix and surrounding non-tumor cells. The development of accurate 3D models may become beneficial to decrease the use of laboratory animals in scientific research, in accordance with the European Union's regulation on the 3R rule (Replacement, Reduction, Refinement). This review focuses on the impact of 3D cell culture models on cancer research, discussing their advantages, limitations, and compatibility with high-throughput screenings and automated systems. An insight is also given on the adequacy of the available readouts for the interpretation of the data obtained from the 3D cell culture models. Importantly, we also emphasize the need for the incorporation of additional and complementary microenvironment elements on the design of 3D cell culture models, towards improved predictive value of drug efficacy.

Factors associated with successful phase III trials for solid tumors: A systematic review

Background

It is known that the success rates of phase III trials for solid cancers are low. The aim of this study was to investigate factors related to trial design and operation that were associated with the probability of the success of phase III trials for solid cancers based on the latest comprehensive data.

Methods

Relevant clinical trials, started between September 2007 and December 2017, were retrieved from ClinicalTrials.gov. Then, variables related to the selected trials such as types of primary endpoint and duration of trial enrollment were collected from the literature and ClinicalTrials.gov. Based on the collected data, a multivariate logistic regression analysis was conducted to find factors associated with the successful results.

Results

Four hundred phase III trials were found eligible for the study. Unsuccessful trials were 207 and successful trials were 193. As a result of multivariate logistic regression analysis, factors that presented a statistically significant relationship were primary endpoint (Odds ratio [OR]: 2.79 [95% CI: 1.59–4.89]), control arm (OR: 3.06 [95% CI: 1.39–6.73]), start year of trial (OR: 3.28 [95% CI: 1.87–5.77]), and duration of trial enrollment (OR: 0.77 [95% CI: 0.60–0.99]).

Conclusion

Type of primary endpoints (time-to-event endpoints other than overall survival), control arm (treatments with lower evidence level, placebo or best supportive care), and duration of trial enrollment (faster enrollment speed) were associated with phase III trial success.

Harnessing the predictive power of preclinical models for oncology drug development

Recent progress in understanding the molecular basis of cellular processes, identification of promising therapeutic targets and evolution of the regulatory landscape makes this an exciting and unprecedented time to be in the field of oncology drug development. However, high costs, long development timelines and steep rates of attrition continue to afflict the drug development process. Lack of predictive preclinical models is considered one of the key reasons for the high rate of attrition in oncology. Generating meaningful and predictive results preclinically requires a firm grasp of the relevant biological questions and alignment of the model systems that mirror the patient context. In doing so, the ability to conduct both forward translation, the process of implementing basic research discoveries into practice, as well as reverse translation, the process of elucidating the mechanistic basis of clinical observations, greatly enhances our ability to develop effective anticancer treatments. In this Review, we outline issues in preclinical-to-clinical translatability of molecularly targeted cancer therapies, present concepts and examples of successful reverse translation, and highlight the need to better align tumour biology in patients with preclinical model systems including tracking of strengths and weaknesses of preclinical models throughout programme development.

The Impact of Excluding Nonrandomized Studies From Systematic Reviews in Rare Diseases: "The Example of Meta-Analyses Evaluating the Efficacy and Safety of Enzyme Replacement Therapy in Patients With Mucopolysaccharidosis"

Nonrandomized studies are usually excluded from systematic reviews. This could lead to loss of a considerable amount of information on rare diseases. In this article, we explore the impact of excluding nonrandomized studies on the generalizability of meta-analyses results on mucopolysaccharidosis (MPS) disease. A comprehensive search of systematic reviews on MPS patients up to May 2020 was carried out (CRD42020191217). The primary endpoint was the rate of patients excluded from systematic reviews if only randomized studies were considered. Secondary outcomes included the differences in patient and study characteristics between randomized and nonrandomized studies, the methods used to combine data from studies with different designs, and the number of patients excluded from systematic reviews if case reports were not considered. More than 50% of the patients analyzed have been recruited in nonrandomized studies. Patient characteristics, duration of follow-up, and the clinical outcomes evaluated differ between the randomized and nonrandomized studies. There are feasible strategies to combine the data from different randomized and nonrandomized designs. The analyses suggest the relevance of including case reports in the systematic reviews, since the smaller the number of patients in the reference population, the larger the selection bias associated to excluding case reports. Our results recommend including nonrandomized studies in the systematic reviews of MPS to increase the representativeness of the results and to avoid a selection bias. The recommendations obtained from this study should be considered when conducting systematic reviews on rare diseases.

Natural Language Processing for Patient Selection in Phase I or II Oncology Clinical Trials

PURPOSE

Early discontinuation affects more than one third of patients enrolled in early-phase oncology clinical trials. Early discontinuation is deleterious both for the patient and for the study, by inflating its duration and associated costs. We aimed at predicting the successful screening and dose-limiting toxicity period completion (SSD) from automatic analysis of consultation reports.

MATERIALS AND METHODS

We retrieved the consultation reports of patients included in phase I and/or phase II oncology trials for any tumor type at Gustave Roussy, France. We designed a preprocessing pipeline that transformed free text into numerical vectors and gathered them into semantic clusters. These document-based semantic vectors were then fed into a machine learning model that we trained to output a binary prediction of SSD status.

RESULTS

Between September 2012 and July 2020, 56,924 consultation reports were used to build the dictionary and 1,858 phase I or II inclusion reports were used to train (72%), validate (14%), and test (14%) a random forest model. Preprocessing could efficiently cluster words with semantic proximity. On the unseen test cohort of 264 consultation reports, the performances of the model reached: F1 score 0.80, recall 0.81, and area under the curve 0.88. Using this model, we could have reduced the screen fail rate (including dose-limiting toxicity period) from 39.8% to 12.8% (relative risk, 0.322; 95% CI, 0.209 to 0.498; P < .0001) within the test cohort. Most important semantic clusters for predictions comprised words related to hematologic malignancies, anatomopathologic features, and laboratory and imaging interpretation.

CONCLUSION

Machine learning with semantic conservation is a promising tool to assist physicians in selecting patients prone to achieve SSD in early-phase oncology clinical trials.

Human biomimetic liver microphysiology systems in drug development and precision medicine

Microphysiology systems (MPS), also called organs-on-chips and tissue chips, are miniaturized functional units of organs constructed with multiple cell types under a variety of physical and biochemical environmental cues that complement animal models as part of a new paradigm of drug discovery and development. Biomimetic human liver MPS have evolved from simpler 2D cell models, spheroids and organoids to address the increasing need to understand patient-specific mechanisms of complex and rare diseases, the response to therapeutic treatments, and the absorption, distribution, metabolism, excretion and toxicity of potential therapeutics. The parallel development and application of transdisciplinary technologies, including microfluidic devices, bioprinting, engineered matrix materials, defined physiological and pathophysiological media, patient-derived primary cells, and pluripotent stem cells as well as synthetic biology to engineer cell genes and functions, have created the potential to produce patient-specific, biomimetic MPS for detailed mechanistic studies. It is projected that success in the development and maturation of patient-derived MPS with known genotypes and fully matured adult phenotypes will lead to advanced applications in precision medicine. In this Review, we examine human biomimetic liver MPS that are designed to recapitulate the liver acinus structure and functions to enhance our knowledge of the mechanisms of disease progression and of the absorption, distribution, metabolism, excretion and toxicity of therapeutic candidates and drugs as well as to evaluate their mechanisms of action and their application in precision medicine and preclinical trials.

Missing the target in cancer therapy

The deployment of molecular biomarkers that are indicative of sensitivity to tumor-targeted or immune-targeted cancer therapies improves the outcome of individual patients and increases the chances of successful drug approval. However, for many lethal malignancies, the majority of clinical trials are conducted with patients who do not have biomarkers and hence they miss the target.

The regulatory function of mixed lineage kinase 3 in tumor and host immunity

Protein kinases are the second most sought-after G-protein coupled receptors as drug targets because of their overexpression, mutations, and dysregulated catalytic activities in various pathological conditions. Till 2019, 48 protein kinase inhibitors have received FDA approval for the treatment of multiple illnesses, of which the majority of them are indicated for different malignancies. One of the attractive sub-group of protein kinases that has attracted attention for drug development is the family members of MAPKs that are recognized to play significant roles in different cancers. Several inhibitors have been developed against various MAPK members; however, none of them as monotherapy has shown sustainable efficacy. One of the MAPK members, called Mixed Lineage Kinase 3 (MLK3), has attracted considerable attention due to its role in inflammation and neurodegenerative diseases; however, its role in cancer is an emerging area that needs more investigation. Recent advances have shown that MLK3 plays a role in cancer cell survival, migration, drug resistance, cell death, and tumor immunity. This review describes how MLK3 regulates different MAPK pathways, cancer cell growth and survival, apoptosis, and host's immunity. We also discuss how MLK3 inhibitors can potentially be used along with immunotherapy for different malignancies.

U.S. FDA Approved Drugs from 2015-June 2020: A Perspective

In the present work, we report compilation and analysis of 245 drugs, including small and macromolecules approved by the U.S. FDA from 2015 until June 2020. Nearly 29% of the drugs were approved for the treatment of various types of cancers. Other major therapeutic areas of focus were infectious diseases (14%); neurological conditions (12%); and genetic, metabolic, and cardiovascular disorders (7-8% each). Itemization of the approved drugs according to the year of approval, sponsor, target, chemical class, major drug-metabolizing enzyme(s), route of administration/elimination, and drug-drug interaction liability (perpetrator or/and victim) is presented and discussed. An effort has been made to analyze the pharmacophores to identify the structural (e.g., aromatic, heterocycle, and aliphatic), elemental (e.g., boron, sulfur, fluorine, phosphorus, and deuterium), and functional group (e.g., nitro drugs) diversity among the approved drugs. Further, descriptor-based chemical space analysis of FDA approved drugs and several strategies utilized for optimizing metabolism leading to their discoveries have been emphasized. Finally, an analysis of drug-likeness for the approved drugs is presented.

Identification of drug compounds for keloids and hypertrophic scars: drug discovery based on text mining and DeepPurpose

Background

Keloids (KL) and hypertrophic scars (HS) are forms of abnormal cutaneous scarring characterized by excessive deposition of extracellular matrix and fibroblast proliferation. Currently, the efficacy of drug therapies for KL and HS is limited. The present study aimed to investigate new drug therapies for KL and HS by using computational methods.

Methods

Text mining and GeneCodis were used to mine genes closely related to KL and HS. Protein-protein interaction analysis was performed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. The selection of drugs targeting the genes closely related to KL and HS was carried out using Pharmaprojects. Drug-target interaction prediction was performed using DeepPurpose, through which candidate drugs with the highest predicted binding affinity were finally obtained.

Results

Our analysis using text mining identified 69 KL- and HS-related genes. Gene enrichment analysis generated 25 genes, representing 7 pathways and 130 targeting drugs. DeepPurpose recommended 14 drugs as the final drug list, including 2 phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) inhibitors, 10 prostaglandin-endoperoxide synthase 2 (PTGS2) inhibitors and 2 vascular endothelial growth factor A (VEGFA) antagonists.

Conclusions

Drug discovery using in silico text mining and DeepPurpose may be a powerful and effective way to identify drugs targeting the genes related to KL and HS.

A numerical study on tumor-on-chip performance and its optimization for nanodrug-based combination therapy

Microfluidic devices, such as the tumor-on-a-chip (ToC), allow for the delivery of multiple drugs as desired for various therapies such as cancer treatment. Due to the complexity involved, visualizing, and gaining knowledge of the performance of such devices through experimentation alone is difficult if not impossible. In this paper, we performed a numerical simulation study on ToC performance, which focuses on the ability to combine multiple nanodrugs and optimized ToC performance. The numerical simulations of the chip performance were performed based on the typical chip design and operating parameters, as well as the established governing equations, boundary conditions, and fluid-structure interaction. The effect of cell injection time and position, inlet flow rate, number of inlets, medium viscosity, and cell concentration on the chip performance in terms of shear stress and cell distribution were examined. The results illustrate the profound effect of operation parameters, thus allowing for rigorously determining operational parameters to prevent spheroids ejection from microwells and to restrict the shear stresses within a physiological range. Also, the results show that triple-inlets can increase the uniformity of cell distribution in comparison with single or double inlets. Based on the simulation results, the architecture of the primary ToC was further optimized, resulting in a novel design that enables applying multiple, yet simultaneous, nanodrugs with optimal drug combination as desired for an individual patient. Furthermore, our simulations on the optimized chip showed a uniform cell distribution required for uniform-sized tumor spheroids generation, and complete medium exchange. Taken together, this study not only illustrates that numerical simulations are effective to visualize the ToCs performance, but also develops a novel ToC design optimized for nanodrug-based combination therapy.

RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials

MOTIVATION

When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting.

RESULTS

We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit. However, we find that RAINFOREST is able to identify a subgroup comprising 27.7% of the patients that do benefit, with a hazard ratio of 0.69 (P = 0.04) in favor of cetuximab. The method is not specific to colorectal cancer and could aid in reanalysis of clinical trial data and provide a more personalized approach to cancer treatment, also when there is no clear link between a single variant and treatment benefit.

AVAILABILITY AND IMPLEMENTATION

The R code used to produce the results in this paper can be found at github.com/jubels/RAINFOREST. A more configurable, user-friendly Python implementation of RAINFOREST is also provided. Due to restrictions based on privacy regulations and informed consent of participants, phenotype and genotype data of the CAIRO2 trial cannot be made freely available in a public repository. Data from this study can be obtained upon request. Requests should be directed toward Prof. Dr. H.J. Guchelaar (h.j.guchelaar@lumc.nl).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Chemo-physical Strategies to Advance the in Vivo Functionality of Targeted Nanomedicine: The Next Generation

The past few decades have witnessed an evolution of nanomedicine from biologically inert entities to more smart systems, aimed at advancing in vivo functionality. However, we should recognize that most systems still rely on reasonable explanation-including some over-explanation-rather than definitive evidence, which is a watershed radically determining the speed and extent of advancing nanomedicine. Probing nano-bio interactions and desirable functionality at the tissue, cellular, and molecular levels is most frequently overlooked. Progress toward answering these questions will provide instructive insight guiding more effective chemo-physical strategies. Thus, in the next generation, we argue that much effort should be made to provide definitive evidence for proof-of-mechanism, in lieu of creating many new and complicated systems for similar proof-of-concept.

Assessing Drug Development Risk Using Big Data and Machine Learning

Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.

Inclusion of non-inferiority analysis in superiority-based clinical trials with single-arm, two-stage Simon's design

INTRODUCTION

Non-inferiority (NI) analysis is not usually considered in the early phases of clinical development. In some negative phase II trials, a post-hoc NI analysis justified additional phase III trials that were successful. However, the risk of false positive achievements was not controlled in these early phase analyses. We propose to preplan NI analyses in superiority-based Simon's two-stage designs to control type I and II error rates.

METHODS

Simulations have been proposed to assess the control of type I and II errors rates with this method. A total of 12,768 two-stage Simon's design trials were constructed based on different assumptions of rejection response probability, desired response probability, type I and II errors, and NI margins. P-value and type II error were calculated with stochastic ordering using Uniformly Minimum Variance Unbiased Estimator. Type I and II errors were simulated using the Monte Carlo method. The agreement between calculated and simulated values was analyzed with Bland-Altman plots.

RESULTS

We observed the same level of agreement between calculated and simulated type I and II errors from both two-stage Simon's superiority designs and designs in which NI analysis was allowed. Different examples has been proposed to explain the utility of this method.

CONCLUSION

Inclusion of NI analysis in superiority-based single-arm clinical trials may be useful for weighing additional factors such as safety, pharmacokinetics, pharmacodynamic, and biomarker data while assessing early efficacy. Implementation of this strategy can be achieved through simple adaptations to existing designs for one-arm phase II clinical trials.

Targeting CREB in Cancer Therapy: A Key Candidate or One of Many? An Update

Simple Summary

Only 5% of all drug-related targets currently move from preclinical to clinical in cancer, and just some of them achieve patient’s bedside. Among others, intratumor heterogeneity and preclinical cancer model limitations actually represent the main reasons for this failure. Cyclic-AMP response element-binding protein (CREB) has been defined as a proto-oncogene in different tumor types, being involved in maintenance and progression. Due to its relevance in tumor pathophysiology, many CREB inhibitor compounds have been developed and tested over the years. Herein, we examine the current state-of-the-art of both CREB and CREB inhibitors in cancer, retracing some of the most significant findings of the last years. While the scientific statement confers on CREB a proactive role in cancer, its therapeutic potential is still stuck at laboratory bench. Therefore, pursuing every concrete result to achieve CREB inhibition in clinical might give chance and future to cancer patients worldwide.

Abstract

Intratumor heterogeneity (ITH) is considered the major disorienting factor in cancer treatment. As a result of stochastic genetic and epigenetic alterations, the appearance of a branched evolutionary shape confers tumor plasticity, causing relapse and unfavorable clinical prognosis. The growing evidence in cancer discovery presents to us “the great paradox” consisting of countless potential targets constantly discovered and a small number of candidates being effective in human patients. Among these, cyclic-AMP response element-binding protein (CREB) has been proposed as proto-oncogene supporting tumor initiation, progression and metastasis. Overexpression and hyperactivation of CREB are frequently observed in cancer, whereas genetic and pharmacological CREB downregulation affects proliferation and apoptosis. Notably, the present review is designed to investigate the feasibility of targeting CREB in cancer therapy. In particular, starting with the latest CREB evidence in cancer pathophysiology, we evaluate the advancement state of CREB inhibitor design, including the histone lysine demethylases JMJD3/UTX inhibitor GSKJ4 that we newly identified as a promising CREB modulator in leukemia cells. Moreover, an accurate analysis of strengths and weaknesses is also conducted to figure out whether CREB can actually represent a therapeutic candidate or just one of the innumerable preclinical cancer targets.

Exploratory Analysis of the Factors Associated With Success Rates of Confirmatory Randomized Controlled Trials in Cancer Drug Development

This study examined the outcomes of recent confirmatory randomized controlled trials (RCTs) in phase III that were initiated between 2005 and 2017 for oncologic drugs in the United States and identified several factors that were associated with the success of RCTs. Our regression analysis showed that studies with progression‐free survival or response rate as primary end point were more likely to succeed than studies with overall survival (odds ratio (OR) = 2.94 and 6.23, respectively). The status of development was also linked with success rates. Studies for non‐lead indication tended to have lower success rates than studies for lead indication (OR = 0.68). Studies for first‐line therapy were observed to have low success rates compared with studies for post second‐line therapies (OR = 0.37). Studies for which strong prior evidence was not listed in their publication tended to be more successful than studies that followed rigorous RCTs or single arm studies for the indication. These results suggest that historical success rates may reflect not only the important features of trials, which can be observed directly from study design and results, but also the background status of trials in clinical development pathways.

A novel workflow for three-dimensional analysis of tumour cell migration

The limitations of two-dimensional analysis in three-dimensional (3D) cellular imaging impair the accuracy of research findings in biological studies. Here, we report a novel 3D approach to acquisition, analysis and interpretation of tumour spheroid images. Our research interest in mesenchymal-amoeboid transition led to the development of a workflow incorporating the generation and analysis of 3D data with instant structured illumination microscopy and a new ImageJ plugin.

Pharmacodynamics effects of CDK4/6 inhibitor LEE011 (ribociclib) in high-risk, localised prostate cancer: a study protocol for a randomised controlled phase II trial (LEEP study: LEE011 in high-risk, localised Prostate cancer)

INTRODUCTION

Despite the development of new therapies for advanced prostate cancer, it remains the most common cause of cancer and the second leading cause of cancer death in men. It is critical to develop novel agents for the treatment of prostate cancer, particularly those that target aspects of androgen receptor (AR) signalling or prostate biology other than inhibition of androgen synthesis or AR binding. Neoadjuvant pharmacodynamic studies allow for a rational approach to the decisions regarding which targeted therapies should progress to phase II/III trials. CDK4/6 inhibitors have evidence of efficacy in breast cancer, and have been shown to have activity in preclinical models of hormone sensitive and castrate resistant prostate cancer. The LEEP trial aims to assess the pharmacodynamic effects of LEE011 (ribociclib), an orally bioavailable and highly selective CDK4/6 inhibitor, in men undergoing radical prostatectomy for high-risk, localised prostate cancer.

METHODS AND ANALYSIS

The multicentre randomised, controlled 4:1 two-arm, phase II, open label pharmacodynamic study will recruit 47 men with high risk, localised prostate cancer who are planned to undergo radical prostatectomy. Participants who are randomised to receive the study treatment will be treated with LEE011 400 mg daily for 21 days for one cycle. The primary endpoint is the frequency of a 50% reduction in Ki-67 proliferation index from the pretreatment prostate biopsy compared to that present in prostate cancer tissue from radical prostatectomy. Secondary and tertiary endpoints include pharmacodynamic assessment of CDK4/6 cell cycle progression via E2F levels, apoptotic cell death by cleaved caspase-3, changes in serum and tumour levels of Prostate Specific Antigen (PSA), pathological regression, safety via incidence of adverse events and exploratory biomarker analysis.

ETHICS AND DISSEMINATION

The protocol was approved by a central ethics review committee (St Vincent's Hospital HREC) for all participating sites (HREC/17/SVH/294). Results will be disseminated in peer-reviewed journals and at scientific conferences.

DRUG SUPPLY

Novartis.

PROTOCOL VERSION

2.0, 30 May 2019 TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12618000354280).

Identification of key candidate genes and molecular pathways in white fat browning: an anti-obesity drug discovery based on computational biology

Background

Obesity—with its increased risk of obesity-associated metabolic diseases—has become one of the greatest public health epidemics of the twenty-first century in affluent countries. To date, there are no ideal drugs for treating obesity. Studies have shown that activation of brown adipose tissue (BAT) can promote energy consumption and inhibit obesity, which makes browning of white adipose tissue (WAT) a potential therapeutic target for obesity. Our objective was to identify genes and molecular pathways associated with WAT and the activation of BAT to WAT browning, by using publicly available data and computational tools; this knowledge might help in targeting relevant signaling pathways for treating obesity and other related metabolic diseases.

Results

In this study, we used text mining to find out genes related to brown fat and white fat browning. Combined with biological process and pathway analysis in GeneCodis and protein-protein interaction analysis by using STRING and Cytoscape, a list of high priority target genes was developed. The Human Protein Atlas was used to analyze protein expression. Candidate drugs were derived on the basis of the drug-gene interaction analysis of the final genes. Our study identified 18 genes representing 6 different pathways, targetable by a total of 33 drugs as possible drug treatments. The final list included 18 peroxisome proliferator-activated receptor gamma (PPAR-γ) agonists, 4 beta 3 adrenoceptor (β3-AR) agonists, 1 insulin sensitizer, 3 insulins, 6 lipase clearing factor stimulants and other drugs.

Conclusions

Drug discovery using in silico text mining, pathway, and protein-protein interaction analysis tools may be a method of exploring drugs targeting the activation of brown fat or white fat browning, which provides a basis for the development of novel targeted therapies as potential treatments for obesity and related metabolic diseases.

Agreement between results of meta-analyses from case reports and clinical studies, regarding efficacy and safety of idursulfase therapy in patients with mucopolysaccharidosis type II (MPS-II). A new tool for evidence-based medicine in rare diseases

BACKGROUND

A preliminary exploratory study shows solid agreement between the results of case reports and clinical study meta-analyses in mucopolysaccharidosis Type I (MPS-I) adult patients. The aim of the present study is to confirm previous results in another patient population, suffering from mucopolysaccharidosis Type II (MPS-II).

METHODS

A systematic review and meta-analysis of case reports published by April 2018 was conducted for MPS-II patients treated with enzyme replacement therapy (ERT). The study is reported in accordance with PRISMA and MOOSE guidelines (PROSPERO database code CRD42018093408). The assessed population and outcomes were the same as previously analyzed in a meta-analysis of MPS-II clinical studies. The primary endpoint was the percent of clinical cases showing improvement in efficacy outcome, or no harm in safety outcome after ERT initiation. A restrictive procedure to aggregate case reports, by selecting standardized and well-defined outcomes, was proposed. Different sensitivity analyses were able to evaluate the robustness of results.

RESULTS

Every outcome classified as "acceptable evidence group" in our case report meta-analysis had been graded as "moderate strength of evidence" in the aforementioned meta-analysis of clinical studies. Sensitivity, specificity, and positive-negative predictive values for results of both meta-analyses reached 100%, and were deemed equivalent.

CONCLUSIONS

Aggregating case reports quantitatively, rather than analyzing them qualitatively, may improve conclusions in rare diseases and personalized medicine. Additionally, we propose some methods to evaluate publication bias and heterogeneity of the included studies in a meta-analysis of case reports.

Phase I trials as valid therapeutic options for patients with cancer

For many years, oncology phase I trials have been referred to as 'toxicity trials' and have been believed to have low clinical utility other than that of establishing the adverse event profile of novel therapeutic agents. The traditional distinction of clinical trials into three phases has been challenged in the past few years by the introduction of targeted therapies and immunotherapies into the routine management of patients with cancer. This transformation has especially affected early phase trials, leading to the current situation in which response rates are increasingly reported from phase I trials. In this Perspectives, we highlight key elements of phase I trials and discuss how each one of them contributes to a new paradigm whereby preliminary measurements of the clinical benefit from a novel treatment can be obtained in current phase I trials, which can therefore be considered to have a therapeutic intent.

Factors Affecting Combination Trial Success (FACTS): Investigator Survey Results on Early-Phase Combination Trials

Experimental therapeutic oncology agents are often combined to circumvent tumor resistance to individual agents. However, most combination trials fail to demonstrate sufficient safety and efficacy to advance to a later phase. This study collected survey data on phase 1 combination therapy trials identified from ClinicalTrials.gov between January 1, 2003 and November 30, 2017 to assess trial design and the progress of combinations toward regulatory approval. Online surveys (N = 289, 23 questions total) were emailed to Principal Investigators (PIs) of early-phase National Cancer Institute and/or industry trials; 263 emails (91%) were received and 113 surveys completed (43%). Among phase 1 combination trials, 24.9% (95%CI: 15.3%, 34.4%) progressed to phase 2 or further; 18.7% (95%CI: 5.90%, 31.4%) progressed to phase 3 or regulatory approval; and 12.4% (95%CI: 0.00%, 25.5%) achieved regulatory approval. Observations of “clinical promise” in phase 1 combination studies were associated with higher rates of advancement past each milestone toward regulatory approval (cumulative OR = 11.9; p = 0.0002). Phase 1 combination study designs were concordant with Clinical Trial Design Task Force (CTD-TF) Recommendations 79.6% of the time (95%CI: 72.2%, 87.1%). Most discordances occurred where no plausible pharmacokinetic or pharmacodynamic interactions were expected. Investigator-defined “clinical promise” of a combination is associated with progress toward regulatory approval. Although concordance between study designs of phase 1 combination trials and CTD-TF Recommendations was relatively high, it may be beneficial to raise awareness about the best study design to use when no plausible pharmacokinetic or pharmacodynamic interactions are expected.