Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles

Myocardial infarction causes cardiac tissue damage and the release of damage-associated molecular patterns leads to activation of the immune system, production of inflammatory mediators, and migration of various cells to the site of infarction. This complex response further aggravates tissue damage by generating oxidative stress, but it eventually heals the infarction site with the formation of fibrotic tissue and left ventricle remodeling. However, the limited self-renewal capability of cardiomyocytes cannot support sufficient cardiac tissue regeneration after extensive myocardial injury, thus, leading to an irreversible decline in heart function. Approaches to improve cardiac tissue regeneration include transplantation of stem cells and delivery of inflammation modulatory and wound healing factors. Nevertheless, the harsh environment at the site of infarction, which consists of, but is not limited to, oxidative stress, hypoxia, and deficiency of nutrients, is detrimental to stem cell survival and the bioactivity of the delivered factors. The use of biomaterials represents a unique and innovative approach for protecting the loaded factors from degradation, decreasing side effects by reducing the used dosage, and increasing the retention and survival rate of the loaded cells. Biomaterials with loaded stem cells and immunomodulating and tissue-regenerating factors can be used to ameliorate inflammation, improve angiogenesis, reduce fibrosis, and generate functional cardiac tissue. In this review, we discuss recent findings in the utilization of biomaterials to enhance cytokine/growth factor and stem cell therapy for cardiac tissue regeneration in small animals with myocardial infarction.

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“…These include a small heart size and anatomical differences in the coronary artery and conduction system. [ 126 , 127 , 128 ].…”

Section: Biomaterials Loaded With Stem Cells For Cardiac Tissue Rementioning

confidence: 99%

Biomaterials Loaded with Growth Factors/Cytokines and Stem Cells for Cardiac Tissue Regeneration

Smagul

Kim

Smagulova

et al. 2020

IJMS

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“…It is estimated to cost over 2.6 billion USD and take over a decade to develop an oncology therapeutic [1]. These soaring costs are mostly a result of money invested in the 90% of candidate therapies that fail at the late stages of drug development, between phase 1 trials and regulatory approval [2]. AI is projected to be the foundation for an era of quicker, cheaper, and more efficient drug discovery and development.…”

Section: Introductionmentioning

confidence: 99%

Introduction: An Overview of AI in Oncology Drug Discovery and Development

Linton-Reid¹

2020

Artificial Intelligence in Oncology Drug Discovery and Development

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Artificial intelligence (AI) has been termed the machine for the fourth industrial revolution. One of the main challenges in drug discovery and development is the time and costs required to sustain the drug development pipeline. It is estimated to cost over 2.6 billion USD and take over a decade to develop cancer therapeutics. This is primarily due to the high numbers of candidate drugs failing at late drug development stages. Many sizable pharmaceutical and biotech companies have made considerable investments in AI. This is primarily due to recent advancements in AI, which have displayed the possibility of rapid low-cost drug discovery and development. This overview provides a general introduction to AI in drug discovery and development. This chapter will describe the conventional oncology drug discovery pipeline and its associated challenges. Fundamental AI concepts are also introduced, alongside historical and modern advancements within AI and drug discovery and development. Lastly, the future potential and challenges of AI in oncology are discussed.

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“…Furthermore, whilst potentially beneficial treatments have been identified in individual trials at the preclinical stage, intravenous thrombolysis remains the only regulatory approved treatment for ischaemic stroke [33,39,40]. This lack of transferable results from the preclinical to clinical stage highlights a major shortcoming for the generalizability of stroke animal models and is emblematic of translation failures generally across preclinical studies [6,7,33,34].…”

Section: Introductionmentioning

confidence: 99%

Embrace heterogeneity to improve reproducibility: A perspective from meta-analysis of variation in preclinical research

Usui

Macleod

McCann

et al. 2020

Preprint

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The reproducibility of research results has been a cause of increasing concern to the scientific community. The long-held belief that experimental standardization begets reproducibility has also been recently challenged, with the observation that the reduction of variability within studies can lead to idiosyncratic, lab-specific results that are irreproducible. An alternative approach is to, instead, deliberately introduce heterogeneity; known as "heterogenization" of experimental design. Here, we explore a novel perspective in the heterogenization program in a meta-analysis of variability in observed phenotypic outcomes in both control and experimental animal models of ischaemic stroke. First, by quantifying inter-individual variability across control groups we illustrate that the amount of heterogeneity in disease-state (infarct volume) differs according to methodological approach, for example, in disease-induction methods and disease models. We argue that such methods may improve reproducibility by creating diverse and representative distribution of baseline disease-state in the reference group, against which treatment efficacy is assessed. Second, we illustrate how meta-analysis can be used to simultaneously assess efficacy and stability (i.e., mean effect and among-individual variability). We identify treatments that have efficacy and are generalizable to the population level (i.e. low inter-individual variability), as well as those where there is high inter-individual variability in response; for these latter treatments translation to a clinical setting may require nuance. We argue that by embracing rather than seeking to minimise variability in phenotypic outcomes, we can motivate the shift towards heterogenization and improve both the reproducibility and generalizability of preclinical research.

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Lost in translation: the valley of death across preclinical and clinical divide – identification of problems and overcoming obstacles

Cited by 411 publications

References 125 publications

Biomaterials Loaded with Growth Factors/Cytokines and Stem Cells for Cardiac Tissue Regeneration

Biomaterials Loaded with Growth Factors/Cytokines and Stem Cells for Cardiac Tissue Regeneration

Introduction: An Overview of AI in Oncology Drug Discovery and Development

Embrace heterogeneity to improve reproducibility: A perspective from meta-analysis of variation in preclinical research

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