Success Rate Of Clinical Trials Research Articles

Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018

The mean cost of developing a new drug has been the subject of debate, with recent estimates ranging from $314 million to $2.8 billion. To estimate the research and development investment required to bring a new therapeutic agent to market, using publicly available data. Data were analyzed on new therapeutic agents approved by the US Food and Drug Administration (FDA) between 2009 and 2018 to estimate the research and development expenditure required to bring a new medicine to market. Data were accessed from the US Securities and Exchange Commission, Drugs@FDA database, and ClinicalTrials.gov, alongside published data on clinical trial success rates. Conduct of preclinical and clinical studies of new therapeutic agents. Median and mean research and development spending on new therapeutic agents approved by the FDA, capitalized at a real cost of capital rate (the required rate of return for an investor) of 10.5% per year, with bootstrapped CIs. All amounts were reported in 2018 US dollars. The FDA approved 355 new drugs and biologics over the study period. Research and development expenditures were available for 63 (18%) products, developed by 47 different companies. After accounting for the costs of failed trials, the median capitalized research and development investment to bring a new drug to market was estimated at $985.3 million (95% CI, $683.6 million-$1228.9 million), and the mean investment was estimated at $1335.9 million (95% CI, $1042.5 million-$1637.5 million) in the base case analysis. Median estimates by therapeutic area (for areas with ≥5 drugs) ranged from $765.9 million (95% CI, $323.0 million-$1473.5 million) for nervous system agents to $2771.6 million (95% CI, $2051.8 million-$5366.2 million) for antineoplastic and immunomodulating agents. Data were mainly accessible for smaller firms, orphan drugs, products in certain therapeutic areas, first-in-class drugs, therapeutic agents that received accelerated approval, and products approved between 2014 and 2018. Results varied in sensitivity analyses using different estimates of clinical trial success rates, preclinical expenditures, and cost of capital. This study provides an estimate of research and development costs for new therapeutic agents based on publicly available data. Differences from previous studies may reflect the spectrum of products analyzed, the restricted availability of data in the public domain, and differences in underlying assumptions in the cost calculations.

Single-cell genomic approaches for developing the next generation of immunotherapies.

Recent progress in single-cell genomics urges its application in drug development, particularly of cancer immunotherapies. Current immunotherapy pipelines are focused on functional outcome and simple cellular and molecular readouts. A thorough mechanistic understanding of the cells and pathways targeted by immunotherapy agents is lacking, which limits the success rate of clinical trials. A large leap forward can be made if the immunotherapy target cells and pathways are characterized at high resolution before and after treatment, in clinical cohorts and model systems. This will enable rapid development of effective immunotherapies and data-driven design of synergistic drug combinations. In this Perspective, we discuss how emerging single-cell genomic technologies can serve as an engine for target identification and drug development.

Abstract LB-B01: Investigation of immuno-oncology drugs with humanized mouse models: a panel of technologies to evaluate different pharmacological approaches

Abstract Immunotherapy is one of the most exciting recent breakthroughs in the field of cancer treatment. Different approaches are developed such as cancer vaccines, adoptive cellular immunotherapy or immune checkpoint blockade, and a number have been regulatory approved or are currently investigated in clinic. Effective immunity against cancer involves complex interaction between the tumor, the host and the environment. The assessment of cancer immunotherapy approaches in preclinical settings requires the use of appropriate animal models that sufficiently reflect the physiological situation in humans and that must be chosen carefully to address specific mechanisms of action. In light of evaluating the therapeutic potential of different immunomodulatory agents in mice xenografted with cancer cell lines or patient-derived xenografts, we developed multiple humanization strategies on different immuno-deficient mouse strains. Either human PBMCs, Hematopoietic Stem Cells (HSCs), or specific human immune cells such as Dendritic Cells (DCs), T cells, subset of T cells (e.g. gamma9 delta2 T cells) and NK cells were used, but also the combinations of immune subpopulations such as the co-transfer of autologous T cells and DCs. We will highlight some results of our therapeutic efficacy investigation and evaluation of novel immuno-oncology therapies eg. adoptive cell therapy, bispecific T-cell engager antibody, and vaccine immunomodulatory agents in such humanized mouse models, assessing survival, tumor growth and effects on tumors and immune cells by flow cytometry and immunohistochemistry analyses. All these different humanized models can permit to evidence significant in vivo antitumor efficacy of immunomodulatory agents and might help to increase the success rate in clinical trials. Citation Format: Caroline Mignard, Olivier Duchamp, Josselin Caradec, Francis Bichat, Damien France, Fabrice Viviani, Jean François Mirjolet. Investigation of immuno-oncology drugs with humanized mouse models: a panel of technologies to evaluate different pharmacological approaches [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr LB-B01. doi:10.1158/1535-7163.TARG-19-LB-B01

PREDICT: a checklist for preventing preanalytical diagnostic errors in clinical trials.

Although the importance of guaranteeing a high level of preanalytical quality in routine diagnostic testing has already been largely acknowledged over the past decades, minor emphasis is currently being placed on the fact that accurate performance and standardization of many preanalytical activities are also necessary prerogatives of clinical trials. Reliable evidence exists that clear indications on how to manage the different preanalytical steps are currently lacking in many clinical trials protocols, nor have detailed authoritative documents been published or endorsed on this matter to the best of our knowledge. To fill this gap, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for Preanalytical Phase (WG-PRE) will provide here a specific checklist for preventing preanalytical diagnostic errors in clinical trials (PREDICT), especially focused on covering the most important preanalytical aspects of blood sample management in clinical studies, and thus encompassing test selection, patient preparation, sample collection, management and storage, sample transportation, as well as specimen retrieval before testing. The WG-PRE members sincerely hope that these recommendations will provide a useful contribution for increasing the success rate in clinical trials.

Abstract 1068: How humanized mouse models could be useful for immuno-oncology research

Abstract Immunotherapy is one of the most exiting recent breakthroughs in the field of cancer treatment. Different approaches are developed such as cancer vaccines, adoptive cellular immunotherapy or immune checkpoint blockade, and a number have been regulatory approved or are currently investigated in clinic. Effective immunity against cancer involves complex interaction between the tumor, the host and the environment. The assessment of cancer immunotherapy approaches in preclinical settings requires the use of appropriate animal models that sufficiently reflect the physiological situation in humans and that must be chosen carefully to address specific mechanisms of action. In light of evaluating the therapeutic potential of different immunomodulatory agents in mice xenografted with cancer cell lines or patient-derived xenografts, we developed multiple humanization strategies on different immuno-deficient mouse strains. Either human PBMCs, Hematopoietic Stem Cells (HSCs), or specific human immune cells such as Dendritic Cells (DCs), T cells, subset of T cells (e.g. gamma9 delta2 T cells) and NK cells were used, but also the combinations of immune subpopulations such as the co-transfer of autologous T cells and DCs. We will highlight some results of our therapeutic efficacy investigation and evaluation of novel immuno-oncology therapies eg. adoptive cell therapy, bispecific T-cell engager antibody, and vaccine immunomodulatory agents in such humanized mouse models, assessing survival, tumor growth and effects on tumors and immune cells by flow cytometry and immunohistochemistry analyses. All these different humanized models can permit to evidence significant antitumor efficacy of immunomodulatory agents in vivo and might help improve the success rate in clinical trials. Citation Format: Jean-Francois Mirjolet, Josselin Caradec, Oliver Duchamp, Francis Bichat, Damien France, Caroline Mignard, Fabrice Viviani. How humanized mouse models could be useful for immuno-oncology research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1068.

Tumor microenvironment determines drug efficacy in vitro - apoptotic and anti-inflammatory effects of 15-lipoxygenase metabolite, 13-HpOTrE

Recent studies using 3D scaffolds have emphasized the importance of the surrounding stroma on chemoresistance in drug efficacy screenings. Since 15-lipoxygenase (15-LOX) metabolites reduced growth of breast, colon, prostate, lung and leukemia cancer cells in 2D cell culture, we were intrigued by the direct comparison of 15-LOX metabolite efficacy in 2D and 3D culture including a stroma equivalent. Herein, we studied the effects of 15-LOX metabolites 13-HpOTrE, 13-HpODE, and 15-HpETE on cutaneous squamous cell carcinoma cells.All metabolites reduced the viability of cancer cells in 2D culture below 10% at 100 µM of each substance. 13-HpOTrE, being the most active agent with respect to cytotoxicity and apoptosis was selected for further experiments. Other than with the 2D culture, we did not obverse cell death, neither from lactate dehydrogenase release, nor from morphology when applying 13-HpOTrE onto the surface of the 3D tumor constructs for one week. Next, we investigated the protein expression of peroxisome proliferator activated receptor gamma, for which the ligand is 13-HpOTrE, and Bcl-2 protein, an apoptosis regulator, but did not find any change following 13-HpOTrE administration. However, 13-HpOTrE treatment reduced the release of interleukin-6, bringing it closer to the level of tumor-free constructs.In conclusion, 13-HpOTrE reduces viability of skin cancer cells in 2D cultures only but modulates inflammatory cytokine levels in the corresponding 3D tumor constructs, too. These studies highlight the need for screening of anticancer drugs employing 3D tumors and including tumor microenvironment in the screening process to increase the low success rate of clinical trials in oncology.

Estimating Clinical Trial Success Rates and Related Parameters in Oncology

We extend earlier large-scale studies of clinical trial statistics by focusing on the performance of oncology trials. Using 108,248 data points between January 1, 2005, and September 31, 2018, compiled from the Citeline database, we investigate the duration of clinical trials and compute the probabilities of success of 24,448 oncology drug development programs by disease group. While the overall phase 1 to approval rate for all oncology-related drug development programs is 3.3%, individual disease groups have approval rates ranging from 0% to 10.1%. Similar patterns can be seen for oncology orphan drug development programs, where the overall probability of success ranges from 0% to 8.3%, with an overall average of 1.9%. We find overwhelming evidence that using biomarkers for patient selection is effective in almost all disease groups within oncology, raising the overall probability of success by an average of 13.3%.

Abstract P6-03-04: Successful development of patient-derived orthotopic xenograft models of brain and lung metastases of human breast cancer

Abstract Background: Metastatic breast cancer is the reason that we continue to lose 40,000 women every year in the US. Without appropriate pre-clinical model, success rate of clinical trials continue to suffer. Because syngeneic mouse models utilize murine neoplasm that may not represent human cancer, patient-derived xenografts (PDX) have emerged as a pre-clinical model that maintains human cancer features such as intratumoral heterogeneity. However, there is no established orthotopic PDX model for metastatic breast cancer even though the main cause of death is brain and lung metastasis. Orthotopic brain or lung PDX is expected to reproduce the original tumor microenvironment. We describe our new patient-derived metastasis orthotopic xenograft (PDMOX) mouse models of human breast cancer. Methods: All work was performed in female NSG mice of age 8-12 m. Breast cancer metastatic tumors from brain and lung that had been passaged 3x in mammary fat pads were used. Tumors of 1 mm3 were implanted orthotopically in two forms: solid piece, or minced tissue with 3 μl Matrigel. Tumor growth was monitored by MRI. Results: Two methods for brain PDMOX were compared. “Manual push” method implanted minced tumor through a frontal bone burr hole into right caudate putamen at 4 mm depth using forceps. “Pipette tip” method utilized either a pipetter for minced tissue or Hamilton syringe for solid tissue to inoculate tumor. One hour post-surgical survival was 37.5% (3/8) after “manual push”, and 100% (30/30) after “Pipette tip” method. All tumors engrafted in surviving mice with either method. However, the tumors formed on brain surface and parenchyma invasion was rare after “manual push” method, whereas solid tumor invaded parenchyma by “pipette tip” method. Therefore, it was no surprise to find large variation in tumor growth after “Manual push” (detection time 17±5.0 d, range: 17-26; volume 5.6±21.0 mm3, range 2.8-48.7). One mouse developed ptosis, and 2 out of 3 mice that underwent “Manual push” had sudden death. On the other hand, all mice that underwent “Pipette tip” method lived until tumor grew to 125-200 mm3, without neurological symptoms. These brain tumors could be passaged with 100% success (9/9). For right lung PDMOX, “thoracotomy” and “non-thoracotomy” methods were compared. “Thoracotomy” method implanted a solid tissue using forceps or 8-0 nylon suture, or injected minced tissue 1 mm below pleura. “Non-thoracotomy” method injected minced tissue using 23G needle. One hour post-surgical survival was 30% (9/30, 8/30) after “thoracotomy” method using forceps or suture, resp. However, survival using suture method could be significantly improved to 97% (29/30) by reducing thoracotomy length (<10 vs. ≥10 mm: t test P = 0.003). Post-operative survival was not affected by age, weight, or operation or anesthesia time. On the other hand, all mice after “non-thoracotomy” method survived, but chest wall implantation occurred in 67% (4/6) when the method was performed using a cell line. Conclusion: By simple modifications of surgical techniques, we could establish orthotopic brain and lung xenografts of breast cancer tumors with almost zero mortality and 100% engraftment. Our novel PDMOX models can be powerful tools for preclinical studies. Citation Format: Oshi M, Okano M, Takabe K. Successful development of patient-derived orthotopic xenograft models of brain and lung metastases of human breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P6-03-04.

Corrigendum: Estimation of clinical trial success rates and related parameters.

Corrigendum: Estimation of clinical trial success rates and related parameters.

Development of a Novel 3D Tumor-tissue Invasion Model for High-throughput, High-content Phenotypic Drug Screening

While much progress has been made in the war on cancer, highly invasive cancers such as pancreatic cancer remain difficult to treat and anti-cancer clinical trial success rates remain low. One shortcoming of the drug development process that underlies these problems is the lack of predictive, pathophysiologically relevant preclinical models of invasive tumor phenotypes. While present-day 3D spheroid invasion models more accurately recreate tumor invasion than traditional 2D models, their shortcomings include poor reproducibility and inability to interface with automated, high-throughput systems. To address this gap, a novel 3D tumor-tissue invasion model which supports rapid, reproducible setup and user-definition of tumor and surrounding tissue compartments was developed. High-cell density tumor compartments were created using a custom-designed fabrication system and standardized oligomeric type I collagen to define and modulate ECM physical properties. Pancreatic cancer cell lines used within this model showed expected differential invasive phenotypes. Low-passage, patient-derived pancreatic cancer cells and cancer-associated fibroblasts were used to increase model pathophysiologic relevance, yielding fibroblast-mediated tumor invasion and matrix alignment. Additionally, a proof-of-concept multiplex drug screening assay was applied to highlight this model’s ability to interface with automated imaging systems and showcase its potential as a predictive tool for high-throughput, high-content drug screening.

Poly-ligand profiling differentiates trastuzumab-treated breast cancer patients according to their outcomes

Assessing the phenotypic diversity underlying tumour progression requires the identification of variations in the respective molecular interaction networks. Here we report proof-of-concept for a platform called poly-ligand profiling (PLP) that surveys these system states and distinguishes breast cancer patients who did or did not derive benefit from trastuzumab. We perform tissue-SELEX on breast cancer specimens to enrich single-stranded DNA (ssDNA) libraries that preferentially interact with molecular components associated with the two clinical phenotypes. Testing of independent sample sets verifies the ability of PLP to classify trastuzumab-treated patients according to their clinical outcomes with ROC-AUC of 0.78. Standard HER2 testing of the same patients gives a ROC-AUC of 0.47. Kaplan–Meier analysis reveals a median increase in benefit from trastuzumab-containing treatments of 300 days for PLP-positive compared to PLP-negative patients. If prospectively validated, PLP may increase success rates in precision oncology and clinical trials, thus improving both patient care and drug development.

The importance of endpoint selection: How effective does a drug need to be for success in a clinical trial of a possible Alzheimer\u2019s disease treatment?

To date, Alzheimer’s disease (AD) clinical trials have been largely unsuccessful. Failures have been attributed to a number of factors including ineffective drugs, inadequate targets, and poor trial design, of which the choice of endpoint is crucial. Using data from the Alzheimer’s Disease Neuroimaging Initiative, we have calculated the minimum detectable effect size (MDES) in change from baseline of a range of measures over time, and in different diagnostic groups along the AD development trajectory. The Functional Activities Questionnaire score had the smallest MDES for a single endpoint where an effect of 27% could be detected within 3 years in participants with Late Mild Cognitive Impairment (LMCI) at baseline, closely followed by the Clinical Dementia Rating Sum of Boxes (CDRSB) score at 28% after 2 years in the same group. Composite measures were even more successful than single endpoints with an MDES of 21% in 3 years. Using alternative cognitive, imaging, functional, or composite endpoints, and recruiting patients that have LMCI could improve the success rate of AD clinical trials.

A new, higher estimate for clinical trial success

The largest study yet assessing the success rates of clinical trials for drugs has yielded more optimistic data than previous studies. Andrew W. Lo and colleagues at the MIT Laboratory for Financial Engineering used an automated algorithm to draw data from nearly 186,000 unique trials of over 21,000 compounds, from January 2000 through October 2015. The team found that 13.8% of trials make it from Phase I to approval, a higher figure than the often-touted estimates of 10% (Biostatistics 2018, DOI: 10.1093/biostatistics/kxx069). The figure is even higher—20.9%—if cancer trials are excluded because cancer drugs had the lowest success rate, a mere 3.4%. Cancer trials that used biomarkers had higher success rates, though, and the overall oncology drug approval rate for 2015 was 8.3%. Vaccines for infectious disease and ophthalmology drugs had the highest success rates, with 33.4% and 32.6% of Phase I compounds, respectively, eventually approved. “It is encouraging to

Estimation of clinical trial success rates and related parameters.

SUMMARYPrevious estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.

At the Interface between Invention and Innovation: Increasing Success Rates in Clinical Trials

Turning basic research discoveries into marketable products or technologies is a major challenge requiring individuals to possess a specific skill set. Among these skills, the ability to “translate”, especially between basic and applied research and bridge these areas is likely to be key. We term skills in basic and applied research as “horizontal” while skills in diverse fields are termed “verti-cal”. We develop a framework to define and differentiate between horizontal and vertical skills and discuss how they separately and jointly impact the ability to turn discoveries into products. Drawing on data from 3,927 clinical trials in the pharmaceutical industry, we found that the like- lihood of success increases when the investigators who lead these trials have horizontal skills bal-anced between basic and applied science. Investigators who exhibit vertical skills were found to reduce the success probability of clinical trials, alleviated only if combined with basic research skills. We also found that trials run by teams with at least one investigator with horizontal or ver-tical skills are more likely to succeed than if all team members have a narrower set of skills each.

Development of an Ex Vivo 3D Functional Assay for Novel Drug Discoveries

Abstract Cancer drug discovery is in crisis since only 5.1% of the total drugs entering Phase 1 clinical trials succeed to approval. A reason for the low success rate in clinical trials lies in the initial steps of drug screening using 2D cell cultures and use of immunocompromised mice. Cancer is not just a malignant outgrowth of cells, rather a complex malignancy where the clinical outcome is determined through crosstalk between the malignant, stromal, and immune cells. Furthermore the extracellular matrix is an integral and dynamic part of this crosstalk; examples include TAMs remodeling the ECM and the ECM in turn determining the invasiveness of the malignant cells. since ECM has been shown to be affected by these cells. Thus, a system needs to be designed that introduces all these factors into a drug discovery platform. My aim is to develop an ex vivo assay with humanized synthetic ECM embedded with patient tumor tissue and co-cultured with stromal cells, fibroblast and immune cells isolated from patient. To validate clinical relevance of this complex environment functional assays would be performed to compare drugs response between patients and patient derived tumor. Drug responses on malignant cells will be assessed by 3D high-resolution imaging and computational image morphometrics. My long term goal is to develop a medium throughput novel drug discovery platform that would be guided by machine learning to identify novel chemical compounds.

Reliable And Reproducible Evaluation Of Therapeutic Interventions In The MDX Mouse Model Of DMD

PURPOSE: Preclinical efficacy evaluation in mouse models of human diseases is an important component of drug development. It has been reported that phase II clinical trial success rates have fallen in recent years, with a lack of efficacy being the most frequent reason for failure. Since most of the selected candidate therapeutics have gone through preclinical efficacy testing, this failure could be due to 1) the poor predictive power of disease models, 2) questionable targets, 3) lack of rigor in preclinical trial design, 4) poor control for potential bias, or 5) variable reporting standards. The quality and reproducibility of preclinical trials depend on the thoroughness of the preclinical study, including the design, execution, analysis, and reporting of the preclinical data. Here we have developed a comprehensive phenotyping system to ensure success rate of preclinical candidate evaluations for DMD. METHODS: We subjected mdx mice for following assessments at early (8 weeks) and late (12 months) stages of the disease to represent ambulatory and non-ambulatory stages of the human disease. We assessed Non-Invasive Repeated measures (Open field activity measurement, Grip strength measurement (GSM), Bodyweight, Inflammation assessed by cathepsin activity using optical imaging, and Echocardiography) and Terminal Endpoint measures (Muscle function test (in vitro force contractions), Histological evaluations, Fibrosis measurements, Serum enzymes analysis). RESULTS: Sample size/power estimates showed that EDL muscle specific force, Grip strength and % SF by echocardiography require 5, 11 and 8 mice per group at early stages of the disease and 2 mice/group for all 3 parameters at 12 months’ age. Fibrosis in the heart is less apparent at early stages and require more mice (136/group) in comparison to old age (2mice/group) Cardiac assessment showed that both ejection fraction (EF) and fractional shortening (FS) significantly decreased by 11 months of age in the mdx mice. Evaluation of inflammation in live animals showed that inflammation is more at 2 months than at 12 months. CONCLUSIONS: Our data demonstrates that the quality and reproducibility of preclinical trials depend on not only on the parameter to be analyzed but also on the stage of the disease and sample size required to meaningfully interpret the data.

Rethinking cancer: current challenges and opportunities in cancer research

ABSTRACTCancer therapeutics currently have the lowest clinical trial success rate of all major diseases. Partly as a result of the paucity of successful anti-cancer drugs, cancer will soon be the leading cause of mortality in developed countries. As a disease embedded in the fundamentals of our biology, cancer presents difficult challenges that would benefit from uniting experts from a broad cross-section of related and unrelated fields. Combining extant approaches with novel ones could help in tackling this challenging health problem, enabling the development of therapeutics to stop disease progression and prolong patient lives. This goal provided the inspiration for a recent workshop titled ‘Rethinking Cancer’, which brought together a group of cancer scientists who work in the academic and pharmaceutical sectors of Europe, America and Asia. In this Editorial, we discuss the main themes emerging from the workshop, with the aim of providing a snapshot of key challenges faced by the cancer research community today. We also outline potential strategies for addressing some of these challenges, from understanding the basic evolution of cancer and improving its early detection to streamlining the thorny process of moving promising drug targets into clinical trials.

Shifting the Focus of Preclinical, Murine Atherosclerosis Studies From Prevention to Late-Stage Intervention.

Could improving the design of preclinical murine atherosclerosis studies help increase the success rate of cardiovascular clinical trials? In this Viewpoint, the authors advocate for a change from prevention to intervention study designs and rigorous lesion analyses that they argue will enhance the translational potential of murine atherosclerosis studies. Clinical management of patients with coronary artery disease (CAD) has made great progress by reducing risk factors like low-density lipoprotein cholesterol level, hypertension, and diabetes mellitus. However, late-stage events associated with coronary atherosclerosis including myocardial infarction still account for ≈16% of worldwide mortality and are forecasted to increase in prevalence.1 These events arise from 3 main processes: plaque rupture, plaque erosion, and shedding of calcific nodules. Plaque rupture accounts for the majority of CAD and typically occurs in vulnerable atherosclerotic lesions termed thin-capped fibroatheromas,2 characterized by thin fibrous caps (<65 µm) with high numbers of CD68+ macrophages relative to Acta2+ smooth muscle cells (SMC), large lipid-rich necrotic cores, and often evidence of intraplaque hemorrhage. Much of the difficulty in therapeutically targeting atherosclerosis can be attributed to (1) the nearly ubiquitous prevalence of atherosclerosis development; (2) the slow, clinically-silent progression but acute and often catastrophic presentation of symptoms; and (3) most importantly, the difficulty in identifying patients at high risk for plaque rupture or erosion and subsequent cardiovascular events. Therefore, clinical trials seeking to investigate novel CAD therapies are forced to enroll thousands of patients who already have manifestations of advanced CAD and spend hundreds of millions of dollars to conduct adequately powered phase III clinical trials to rigorously test therapeutic efficacy. And yet, cardiovascular disease trials have one of the lowest success rates of the major medical fields.3 Perhaps, the lack of success can, in part, be attributed to suboptimal design of preclinical studies, which commonly …

Short peptides interfering with signaling pathways as new therapeutic tools for cancer treatment.

Short peptides have many advantages, such as low molecular weight, selectivity for a specific target, organelles or cells with minimal toxicity. We describe properties of short peptides, which interfere with communication networks in tumor cells and within microenvironment of malignant gliomas, the most common brain tumors. We focus on ligand/receptor axes and intracellular signaling pathways critical for gliomagenesis that could be targeted with interfering peptides. We review structures and efficacy of organelle-specific and cell-penetrating peptides and describe diverse chemical modifications increasing proteolytic stability and protecting synthetic peptides against degradation. We report results of application of short peptides in glioma therapy clinical trials, their rises and falls. The most advanced examples of therapeutics such as short interfering peptides combined with cell-penetrating peptides that show good effectiveness in disease models are presented. It is foreseen that identification of peptides with better clinical properties may improve their success rates in clinical trials.