Stem Cell Theory of Cancer: Implications for Translational Research from Bedside to Bench

A stem cell theory of cancer considers genetic makeup in the proper cellular context. It is a unified theory of cancer that unites the genome with the epigenome, links the intracellular with the extracellular, and connects the cellular constituents and compartments with the microenvironment. Although it allies with genomic medicine, it is better aligned with integrated medicine. In this perspective, we focus on translational research in cancer care. We expose some intrinsic fallacies in translational research when it relates to the basic principles of the scientific method in the care of patients with genomic medicine versus integrated medicine. We postulate that genomic medicine may be at the root of many failed efforts in drug development and data reproducibility. We propose an alternate heuristic approach that may expedite the development of safe and effective treatments and minimize the generation of unproductive pharmaceutical products and nonreproducible experimental results. Importantly, a heuristic approach emphasizes the role of a pertinent scientific theory and distinguishes therapy development from drug development, such that we discover not only useful drugs but also better ways to use them in order to optimize patient care and maximize clinical outcomes.

Establishing a centralized data mart from the Rakai community cohort study to improve HIV research in Rakai, Uganda

To improve timely access to quality HIV research data, the Rakai Health Sciences Program (RHSP) Data Mart was developed to store cohort study data from a legacy database platform in a modernized system using standard data management processes. The RHSP Data Mart was developed on a Microsoft SQL Server platform using Microsoft SQL Server Integration Services with custom data mappings and queries. The data mart stores 20+ years of longitudinal HIV research data and includes standard processes for managing data, data dictionary, training materials, and a library of queries to fulfill data requests and load new data from completed survey rounds. The RHSP Data Mart enables efficient querying and analysis of multidimensional research data by simplifying data integration and processing. A sustainable database platform with well-defined data management processes promotes data accessibility and reproducibility, enabling researchers to advance their understanding and management of infectious diseases.

Curated Data In - Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing

New Approach Methodologies (NAMs) that employ artificial intelligence (AI) for predicting adverse effects of chemicals have generated optimistic expectations as alternatives to animal testing. However, the major underappreciated challenge in developing robust and predictive AI models is the impact of the quality of the input data on the model accuracy. Indeed, poor data reproducibility and quality have been frequently cited as factors contributing to the crisis in biomedical research, as well as similar shortcomings in the fields of toxicology and chemistry. In this article, we review the most recent efforts to improve confidence in the robustness of toxicological data and investigate the impact that data curation has on the confidence in model predictions. We also present two case studies demonstrating the effect of data curation on the performance of AI models for predicting skin sensitisation and skin irritation. We show that, whereas models generated with uncurated data had a 7-24% higher correct classification rate (CCR), the perceived performance was, in fact, inflated owing to the high number of duplicates in the training set. We assert that data curation is a critical step in building computational models, to help ensure that reliable predictions of chemical toxicity are achieved through use of the models.

The impact of fraudulent and irreproducible data to the translational research crisis - solutions and implementation

One of the aims of basic neuroscience research is ultimately the development of therapeutics to cure diseases. Funders granting money to research institutions increasingly express interest into how their financial resources are used and look for successful translation in clinical practice. Disappointingly, many findings that started out promising in basic research projects and phase I trials did not live up to the promise of therapeutic efficacy in later phase II or III trials. An inordinately high amount of time and money is thus spent on research that does not always have the required human impact. Potential reasons for these problems are numerous. Although research misconduct occurs and contributes to this shortcoming, it is not the only important factor. Frequently, basic science results turn out to be irreproducible. Irreproducibility, outside of malfeasance, is multifactorial and can include poor experimental design, conduct, statistical analysis, reporting standards, and conceptual flaws. Further confounding problems include an insufficient transferability of animal to human physiology, as well as intersubject group variability, for example, sexual dimorphisms. While the causes of poor data reproducibility are therefore numerous, equally there are many groups that can contribute to improvements in how basic science is reported. Here, we will review how the Journal of Neurochemistry can contribute to increasing the value of preclinical and translational research. Despite a vast amount of very promising basic research findings, these failed to successfully translate into the clinical practice so far. The reasons for this 'data reproducibility crisis' are numerous, for example, rooting in insufficient experimental design, conceptual flaws, incorrect statistical planning and evaluation, incomplete model system that do not adequately reproduce the human physiology, and further reasons discussed in this Review with the aim to present practical solutions that can be implemented by researchers, journals editors, and reviewers. We will also explain measures the Journal of Neurochemistry have implemented to overcome these issues and weaknesses in preclinical research. These includes adherence to the ARRIVE ( www.nc3rs.org) guidelines, NINDS standards (doi: 10.1038/nature11556), and The Transparency and Openness Promotion Guidelines (TOP) Committee guidelines (https://cos.io/top/#TOP). This article is part of the 60th Anniversary special issue.

The reproducibility issue and preclinical academic drug discovery: educational and institutional initiatives fostering translation success

INTRODUCTION

Drug discovery depends critically upon published results from the academy. The reproducibility of preclinical research findings reported by academia in the peer-reviewed literature has been called into question, seriously jeopardizing the value of academic science for inventing therapeutics.

AREAS COVERED

The corrosive effects of the reproducibility issue on drug discovery are considered. Purported correctives imposed upon academia from the outside deal mainly with expunging fraudulent literature and imposing punitive sanctions on the responsible authors. The salutary influence of such post facto actions on the reproducibility of discovery-relevant preclinical research data from academia appears limited. Rather, intentional doctoral-scientist education focused on data replicability and translationally-meaningful science and active participation of university entities charged with research innovation and asset commercialization toward ensuring data quality are advocated as key academic initiatives for addressing the reproducibility issue.

EXPERT OPINION

A mindset shift on the part of both senior university faculty and the academy to take responsibility for the data reproducibility crisis and commit proactively to positive educational, incentivization, and risk- and reward-sharing practices will be fundamental for improving the value of published preclinical academic research to drug discovery.

Antigen clasping by two antigen-binding sites of an exceptionally specific antibody for histone methylation

Antibodies have a well-established modular architecture wherein the antigen-binding site residing in the antigen-binding fragment (Fab or Fv) is an autonomous and complete unit for antigen recognition. Here, we describe antibodies departing from this paradigm. We developed recombinant antibodies to trimethylated lysine residues on histone H3, important epigenetic marks and challenging targets for molecular recognition. Quantitative characterization demonstrated their exquisite specificity and high affinity, and they performed well in common epigenetics applications. Surprisingly, crystal structures and biophysical analyses revealed that two antigen-binding sites of these antibodies form a head-to-head dimer and cooperatively recognize the antigen in the dimer interface. This "antigen clasping" produced an expansive interface where trimethylated Lys bound to an unusually extensive aromatic cage in one Fab and the histone N terminus to a pocket in the other, thereby rationalizing the high specificity. A long-neck antibody format with a long linker between the antigen-binding module and the Fc region facilitated antigen clasping and achieved both high specificity and high potency. Antigen clasping substantially expands the paradigm of antibody-antigen recognition and suggests a strategy for developing extremely specific antibodies.