Drug development: Raise standards for preclinical cancer research

Systematic heterogenization revisited: Increasing variation in animal experiments to improve reproducibility?

Life sciences are currently facing a reproducibility crisis. Originally, the crisis was born out of single alarming failures to reproduce findings at different times and locations. Nowadays, systematic studies indicate that the prevalence of irreproducible research does in fact exceed 50%. Viewed from a rather cynical perspective, Fett's law of the lab "Never replicate a successful experiment" has thus taken on a completely new meaning. In this respect, animal research has come under particular scrutiny, as the stakes are high in terms of both research ethics and societal impact. To counteract this, it is essential to identify sources of poor reproducibility as well as to iron out these failures. We here review the current debate, briefly discuss potential reasons, and summarize steps that have already been undertaken to improve reproducibility in animal research. By the example of classical behavioural phenotyping studies, we particularly highlight the role strict standardization plays in exacerbating the crisis, and review the concept of systematic heterogenization as an alternative strategy to deal with variation in animal studies. Briefly, we argue that systematic variation rather than strict homogenization of experimental conditions benefits the robustness of research findings, and hence their reproducibility. To this end, we will present concrete examples for systematically heterogenized experiments and provide a practical guide on how to apply systematic heterogenization in experimental practice.

Can antibodies be "vegan"? A guide through the maze of today's antibody generation methods

There is no doubt that today's life sciences would look very different without the availability of millions of research antibody products. Nevertheless, the use of antibody reagents that are poorly characterized has led to the publication of false or misleading results. The use of laboratory animals to produce research antibodies has also been criticized. Surprisingly, both problems can be addressed with the same technology. This review charts today's maze of different antibody formats and the various methods for antibody production and their interconnections, ultimately concluding that sequence-defined recombinant antibodies offer a clear path to both improved quality of experimental data and reduced use of animals.

Ethics of gene and cell therapy development for neurologic disorders

In this chapter, I provide a condensed overview of nine recurring policy and ethical challenges encountered with the development of gene and cell therapies for neurologic disease. These include the question of when to initiate first-in-human trials, the ethics and policy of expanded/special access, the conduct of individualized therapy trials, subject selection in trials, designing trials for negative results, unintended effects of interventions on personal identity, comparator choice in randomized trials, consent and therapeutic misestimation, and cost and access for effective therapies. Broadly speaking, I argue that early in their development, the justification of risk in trials of gene and cell therapies derives from the social and scientific value of a trial and not the therapeutic value for trial participation. This generates strong imperatives to justify, design, and report trials appropriately and select patient populations that incur the least burden and opportunity cost for trial participation. Late in intervention development, policy makers must contend with the fact that proven effective interventions will almost certainly amplify strains in healthcare budgets as well as the ethical justifications standing behind reimbursement decisions.

Choice of fusion proteins, expression host, and analytics solves difficult-to-produce protein challenges in discovery research

High quality biological reagents are a prerequisite for pharmacological research. Herein a protein production screening approach, including quality assessment methods, for protein-based discovery research is presented. Trends from 2895 expression constructs representing 253 proteins screened in mammalian and bacterial hosts-91% of which are successfully expressed and purified-are discussed. Mammalian expression combined with the use of solubility-promoting fusion proteins is deemed suitable for most targets. Furthermore, cases utilizing stable cell line generation and choice of fusion protein for higher yield and quality of difficult-to-produce proteins (Leucine-rich repeat-containing G-protein coupled receptor 4 (LGR4) and Neurturin) are presented and discussed. In the case of Neurturin, choice of fusion protein impacted the target binding 80-fold. These results highlight the need for exploration of construct designs and careful Quality Control (QC) of difficult-to-produce protein reagents.

Toward a common standard for data and specimen provenance in life sciences

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Advances in iPSC Technology in Neural Disease Modeling, Drug Screening, and Therapy

Neurodegenerative disorders (NDs) including Alzheimer's Disease, Parkinson's Disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease are all incurable and can only be managed with drugs for the associated symptoms. Animal models of human illnesses help to advance our understanding of the pathogenic processes of diseases. Understanding the pathogenesis as well as drug screening using appropriate disease models of neurodegenerative diseases (NDs) are vital for identifying novel therapies. Human-derived induced pluripotent stem cell (iPSC) models can be an efficient model to create disease in a dish and thereby can proceed with drug screening and identifying appropriate drugs. This technology has many benefits, including efficient reprogramming and regeneration potential, multidirectional differentiation, and the lack of ethical concerns, which open up new avenues for studying neurological illnesses in greater depth. The review mainly focuses on the use of iPSC technology in neuronal disease modeling, drug screening, and cell therapy.

Effects of home-cage elevation on behavioral tests in mice

BACKGROUND

Research reproducibility is a common problem in preclinical behavioral science. Mice are an important animal model for studying human behavioral disorders. Experimenters, processing methods, and rearing environments are the main causes of data variability in behavioral neuroscience. It is likely that mice adapt their behavior according to the environment outside the breeding cage. We speculated that mice housed on elevated shelves and mice housed on low shelves might have differently altered anxiety-like behavior toward heights.

PURPOSE

The purpose of this study was to investigate potential behavioral changes in mice raised at different heights for 3 weeks. Changes in behavior were examined using various experimental tests.

RESULTS

Mice housed on elevated shelves showed reduced anxiety-like behavior in a light/dark traffic test compared with mice housed on low shelves. There were no significant differences between the two groups in terms of activity, exploratory behavior, muscle strength, or depression-like behavior.

CONCLUSIONS

Our results indicate that different cage heights and corresponding light exposure may alter the anxiety-like behavior of mice in response to brightness. Researchers need to carefully control the cage height and light intensity experienced by the mice to produce reproducible test results.

Application and progress of highcontent imaging in molecular biology

Humans have adopted many different methods to explore matter imaging, among which high content imaging (HCI) could conduct automated imaging analysis of cells while maintaining its structural and functional integrity. Meanwhile, as one of the most important research tools for diagnosing human diseases, HCI is widely used in the frontier of medical research, and its future application has attracted researchers' great interests. Here, the meaning of HCI was briefly explained, the history of optical imaging and the birth of HCI were described, and the experimental methods of HCI were described. Furthermore, the directions of the application of HCI were highlighted in five aspects: protein localization changes, gene identification, chemical and genetic analysis, microbiology, and drug discovery. Most importantly, some challenges and future directions of HCI were discussed, and the application and optimization of HCI were expected to be further explored.

Side-by-Side Comparison of Compensation Beads Used in Polychromatic Flow Cytometry

Compensation or unmixing is essential in analyzing multiparameter flow cytometry data. Errors in data correction, either by compensation or unmixing, can completely change the outcome or mislead the researchers. Owing to limited cell numbers, researchers often use synthetic beads to generate the required single stains for the necessary calculation. In this study, the capacity of synthetic beads to influence data correction is evaluated. Corrected data for human peripheral blood cells were generated using cell-based compensation from the same cells or bead-based compensation to identify differences between the methods. These data suggest that correction with beads on full-spectrum and conventional cytometers does not always follow the basic flow compensation/unmixing expectations and alters the data. Overall, the best approach for bead-based correction for an experiment is to evaluate which beads and fluorochromes are most accurately compensated/unmixed.

Developing a Toolbox of Antibodies Validated for Array Tomography-Based Imaging of Brain Synapses

Antibody (Ab)-based imaging techniques rely on reagents whose performance may be application specific. Because commercial antibodies are validated for only a few purposes, users interested in other applications may have to perform extensive in-house antibody testing. Here, we present a novel application-specific proxy screening step to efficiently identify candidate antibodies for array tomography (AT), a serial section volume microscopy technique for high-dimensional quantitative analysis of the cellular proteome. To identify antibodies suitable for AT-based analysis of synapses in mammalian brain, we introduce a heterologous cell-based assay that simulates characteristic features of AT, such as chemical fixation and resin embedding that are likely to influence antibody binding. The assay was included into an initial screening strategy to generate monoclonal antibodies that can be used for AT. This approach simplifies the screening of candidate antibodies and has high predictive value for identifying antibodies suitable for AT analyses. In addition, we have created a comprehensive database of AT-validated antibodies with a neuroscience focus and show that these antibodies have a high likelihood of success for postembedding applications in general, including immunogold electron microscopy. The generation of a large and growing toolbox of AT-compatible antibodies will further enhance the value of this imaging technique.

Use and application of organ-on-a-chip platforms in cancer research

Tumors are a major cause of death worldwide, and much effort has been made to develop appropriate anti-tumor therapies. Existing in vitro and in vivo tumor models cannot reflect the critical features of cancer. The development of organ-on-a-chip models has enabled the integration of organoids, microfluidics, tissue engineering, biomaterials research, and microfabrication, offering conditions that mimic tumor physiology. Three-dimensional in vitro human tumor models that have been established as organ-on-a-chip models contain multiple cell types and a structure that is similar to the primary tumor. These models can be applied to various foci of oncology research. Moreover, the high-throughput features of microfluidic organ-on-a-chip models offer new opportunities for achieving large-scale drug screening and developing more personalized treatments. In this review of the literature, we explore the development of organ-on-a-chip technology and discuss its use as an innovative tool in basic and clinical applications and summarize its advancement of cancer research.

Experience of irreproducibility as a risk factor for poor mental health in biomedical science doctoral students: A survey and interview-based study

High rates of irreproducibility and of poor mental health in graduate students have been reported in the biomedical sciences in the past ten years, but to date, little research has investigated whether these two trends interact. In this study, we ask whether the experience of failing to replicate an expected finding impacts graduate students' mental health. Using an online survey paired with semi-structured qualitative interviews, we examined how often biomedical science doctoral students at a large American public university experienced events that could be interpreted as failures to replicate and how they responded to these experiences. We found that almost all participants had experience with irreproducibility: 84% had failed to replicate their own results, 70% had failed to replicate a colleague's finding, and 58% had failed to replicate a result from the published literature. Participants reported feelings of self-doubt, frustration, and depression while experiencing irreproducibility, and in 24% of cases, these emotional responses were strong enough to interfere with participants' eating, sleeping, or ability to work. A majority (82%) of participants initially believed that the anomalous results could be attributed to their own error. However, after further experimentation, most participants concluded that the original result was wrong (38%), that there was a key difference between the original experiment and their own (17%), or that there was a problem with the protocol (17%). These results suggest that biomedical science graduate students may be biased towards initially interpreting failures to replicate as indicative of a lack of skill, which may trigger or perpetuate feelings of anxiety, depression, or impostorism.

Developing a Toolbox of Antibodies Validated for Array Tomography-Based Imaging of Brain Synapses

Antibody-based imaging techniques rely on reagents whose performance may be application-specific. Because commercial antibodies are validated for only a few purposes, users interested in other applications may have to perform extensive in-house antibody testing. Here we present a novel application-specific proxy screening step to efficiently identify candidate antibodies for array tomography (AT), a serial section volume microscopy technique for high-dimensional quantitative analysis of the cellular proteome. To identify antibodies suitable for AT-based analysis of synapses in mammalian brain, we introduce a heterologous cell-based assay that simulates characteristic features of AT, such as chemical fixation and resin embedding that are likely to influence antibody binding. The assay was included into an initial screening strategy to generate monoclonal antibodies that can be used for AT. This approach simplifies the screening of candidate antibodies and has high predictive value for identifying antibodies suitable for AT analyses. In addition, we have created a comprehensive database of AT-validated antibodies with a neuroscience focus and show that these antibodies have a high likelihood of success for postembedding applications in general, including immunogold electron microscopy. The generation of a large and growing toolbox of AT-compatible antibodies will further enhance the value of this imaging technique.

Meningioma animal models: a systematic review and meta-analysis

BACKGROUND

Animal models are widely used to study pathological processes and drug (side) effects in a controlled environment. There is a wide variety of methods available for establishing animal models depending on the research question. Commonly used methods in tumor research include xenografting cells (established/commercially available or primary patient-derived) or whole tumor pieces either orthotopically or heterotopically and the more recent genetically engineered models-each type with their own advantages and disadvantages. The current systematic review aimed to investigate the meningioma model types used, perform a meta-analysis on tumor take rate (TTR), and perform critical appraisal of the included studies. The study also aimed to assess reproducibility, reliability, means of validation and verification of models, alongside pros and cons and uses of the model types.

METHODS

We searched Medline, Embase, and Web of Science for all in vivo meningioma models. The primary outcome was tumor take rate. Meta-analysis was performed on tumor take rate followed by subgroup analyses on the number of cells and duration of incubation. The validity of the tumor models was assessed qualitatively. We performed critical appraisal of the methodological quality and quality of reporting for all included studies.

RESULTS

We included 114 unique records (78 using established cell line models (ECLM), 21 using primary patient-derived tumor models (PTM), 10 using genetically engineered models (GEM), and 11 using uncategorized models). TTRs for ECLM were 94% (95% CI 92-96) for orthotopic and 95% (93-96) for heterotopic. PTM showed lower TTRs [orthotopic 53% (33-72) and heterotopic 82% (73-89)] and finally GEM revealed a TTR of 34% (26-43).

CONCLUSION

This systematic review shows high consistent TTRs in established cell line models and varying TTRs in primary patient-derived models and genetically engineered models. However, we identified several issues regarding the quality of reporting and the methodological approach that reduce the validity, transparency, and reproducibility of studies and suggest a high risk of publication bias. Finally, each tumor model type has specific roles in research based on their advantages (and disadvantages).

SYSTEMATIC REVIEW REGISTRATION

PROSPERO-ID CRD42022308833.

Advancing reproducibility can ease the 'hard truths' of synthetic biology

Reproducibility has been identified as an outstanding challenge in science, and the field of synthetic biology is no exception. Meeting this challenge is critical to allow the transformative technological capabilities emerging from this field to reach their full potential to benefit the society. We discuss the current state of reproducibility in synthetic biology and how improvements can address some of the central shortcomings in the field. We argue that the successful adoption of reproducibility as a routine aspect of research and development requires commitment spanning researchers and relevant institutions via education, incentivization and investment in related infrastructure. The urgency of this topic pervades synthetic biology as it strives to advance fundamental insights and unlock new capabilities for safe, secure and scalable applications of biotechnology. Graphical Abstract.

Breast cancer organoids and their applications for precision cancer immunotherapy

Immunotherapy is garnering increasing attention as a therapeutic strategy for breast cancer (BC); however, the application of precise immunotherapy in BC has not been fully studied. Further studies on BC immunotherapy have a growing demand for preclinical models that reliably recapitulate the composition and function of the tumor microenvironment (TME) of BC. However, the classic two-dimensional in vitro and animal in vivo models inadequately recapitulate the intricate TME of the original tumor. Organoid models which allow the regular culture of primitive human tumor tissue are increasingly reported that they can incorporate immune components. Therefore, organoid platforms can be used to replicate the BC-TME to achieve the immunotherapeutic reaction modeling and facilitate relevant preclinical trial. In this study, we have investigated different organoid culture methods for BC-TME modeling and their applications for precision immunotherapy in BC.

rs67047829 genotypes of ERV3-1/ZNF117 are associated with lower body mass index in the Polish population

There is now substantial evidence that zinc-finger proteins are implicated in adiposity. Aims were to datamine for high-frequency (near-neutral selection) pretermination-codon (PTC) single-nucleotide polymorphisms (SNPs; n = 141) from a database with > 550,000 variants and analyze possible association with body mass index in a large Polish sample (n = 5757). BMI was regressed (males/females together or separately) against genetic models. Regression for rs67047829 uncovered an interaction-independent association with BMI with both sexes together: mean ± standard deviation, kg/m2: [G];[G], 25.4 ± 4.59 (n = 3650); [G](;)[A], 25.0 ± 4.28 (n = 731); [A];[A], 23.4 ± 3.60 (n = 44); additive model adjusted for age and sex: p = 4.08 × 10-5; beta: - 0.0458, 95% confidence interval (CI) - 0.0732 : - 0.0183; surviving Bonferroni correction; for males: [G];[G], 24.8 ± 4.94 (n = 1878); [G](;)[A], 24.2 ± 4.31 (n = 386); [A];[A], 22.4 ± 3.69 (n = 23); p = 4.20 × 10-4; beta: - 0.0573, CI - 0.0947 : - 0.0199. For average-height males the difference between [G];[G] and [A];[A] genotypes would correspond to ~ 6 kg, suggesting considerable protection against increased BMI. rs67047829 gives a pretermination codon in ERV3-1 which shares an exonic region and possibly promoter with ZNF117, previously associated with adiposity and type-2 diabetes. As this result occurs in a near-neutral Mendelian setting, a drug targetting ERV3-1/ZNF117 might potentially provide considerable benefits with minimal side-effects. This result needs to be replicated, followed by analyses of splice-variant mRNAs and protein expression.

A critical scientific evaluation of a purportedly negative data report - response to Seneviratne et al. 2022

A core principle in the pursuit of scientific knowledge is that science is self-correcting and that important results should be replicable. Hypotheses need to be reinforced, adjusted, or rejected when novel results are obtained. Replication of results confirms hypotheses and enhances their integration into scientific practice. In contrast, publication of substantiated and replicated negative findings (i.e., non-significant or opposite findings) can be the basis to reject erroneous hypotheses or develop alternative strategies for investigation. Replication is a problem in all research fields. The Psychology Reproductivity Project reported that only 36% of 'highly influential' published research in highly ranked journals were reproduced. Similar to positive data, negative data can be flawed. Errors in a negative data set can be based on methodology, statistics, conceptual defects, and flawed peer review. The peer review process has received progressive scrutiny. A large-scale review of the peer review process of manuscripts submitted to the British Medical Journal group indicated that the process could be characterized as inconsistent, inaccurate, and biased. Further analysis indicated that the peer process is easily manipulated, indicative of a failed system, is a major factor behind the lack of replication in science (acceptance of flawed manuscripts), suppresses opposing scientific evidence and views, and causes gaps in and lack of growth of science. Complicating the integrity of scientific publication is the role of Editors/Researchers. Ethical guidelines exist for major publishing houses about editorial ethics, behavior, and practice.

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.

Extraordinary Claims in the Literature on High-Intensity Interval Training (HIIT): I. Bonafide Scientific Revolution or a Looming Crisis of Replication and Credibility?

The literature on high-intensity interval training (HIIT) contains claims that, if true, could revolutionize the science and practice of exercise. This critical analysis examines two varieties of claims: (i) HIIT is effective in improving various indices of fitness and health, and (ii) HIIT is as effective as more time-consuming moderate-intensity continuous exercise. Using data from two recent systematic reviews as working examples, we show that studies in both categories exhibit considerable weaknesses when judged through the prism of fundamental statistical principles. Predominantly, small-to-medium effects are investigated in severely underpowered studies, thus greatly increasing the risk of both type I and type II errors of statistical inference. Studies in the first category combine the volatility of estimates associated with small samples with numerous dependent variables analyzed without consideration of the inflation of the type I error rate. Studies in the second category inappropriately use the p > 0.05 criterion from small studies to support claims of 'similar' or 'comparable' effects. It is concluded that the situation in the HIIT literature is reminiscent of the research climate that led to the replication crisis in psychology. As in psychology, this could be an opportunity to reform statistical practices in exercise science.

A tumour-spheroid manufacturing and cryopreservation process that yields a highly reproducible product ready for direct use in drug screening assays

If it were possible to purchase tumour-spheroids as a standardised product, ready for direct use in assays, this may contribute to greater research reproducibility, potentially reducing costs and accelerating outcomes. Herein, we describe a workflow where uniformly sized cancer tumour-spheroids are mass-produced using microwell culture, cryopreserved with high viability, and then cultured in neutral buoyancy media for drug testing. C4-2B prostate cancer or MCF-7 breast cancer cells amalgamated into uniform tumour-spheroids after 48 h of culture. Tumour-spheroids formed from 100 cells each tolerated the cryopreservation process marginally better than tumour-spheroids formed from 200 or 400 cells. Post-thaw, tumour-spheroid metabolic activity was significantly reduced, suggesting mitochondrial damage. Metabolic function was rescued by thawing the tumour-spheroids into medium supplemented with 10 µM N-Acetyl-l-cysteine (NAC). Following thaw, the neutral buoyancy media, Happy Cell ASM, was used to maintain tumour-spheroids as discrete tissues during drug testing. Fresh and cryopreserved C4-2B or MCF-7 tumour-spheroids responded similarly to titrations of Docetaxel. This protocol will contribute to a future where tumour-spheroids may be available for purchase as reliable and reproducible products, allowing laboratories to efficiently replicate and build on published research, in many cases, making tumour-spheroids simply another cell culture reagent.

Evolution and impact of high content imaging

The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990's. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging: • Evolution and impact of high content imaging: An academic perspective • Evolution and impact of high content imaging: An industry perspective • Evolution of high content image analysis • Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications • The role of data integration and multiomics • The role and evolution of image data repositories and sharing standards • Future perspective of high content imaging hardware and software.

Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs

Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.

The five pillars of computational reproducibility: bioinformatics and beyond

Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

Building a rheumatology biobank for reliable basic/translational research and precision medicine

Research biobanks are non-profit structures that collect, manipulate, store, analyze and distribute systematically organized biological samples and data for research and development purposes. Over the recent years, we have established a biobank, the Rheumatology BioBank (RheumaBank) headed by the Medicine and Rheumatology unit of the IRCCS Istituto Ortopedico Rizzoli (IOR) in Bologna, Italy for the purpose of collecting, processing, storing, and distributing biological samples and associated data obtained from patients suffering from inflammatory joint diseases. RheumaBank is a research biobank, and its main objective is to promote large-scale, high-quality basic, translational, and clinical research studies that can help elucidate pathogenetic mechanisms and improve personalization of treatment choice in patients with rheumatoid arthritis (RA), psoriatic arthritis (PsA) and other spondyloarthritides (SpA).

Translational Pharmacokinetic/Pharmacodynamic Modeling and Simulation of Oxaliplatin and Irinotecan in Colorectal Cancer

Despite the recent advances in this field, there are limited methods for translating organoid-based study results to clinical response. The goal of this study was to develop a pharmacokinetic/pharmacodynamic (PK/PD) model to facilitate the translation, using oxaliplatin and irinotecan treatments with colorectal cancer (CRC) as examples. The PK models were developed using qualified oxaliplatin and irinotecan PK data from the literature. The PD models were developed based on antitumor efficacy data of SN-38 and oxaliplatin evaluated in vitro using tumor organoids. To predict the clinical response, translational scaling of the models was established by incorporating predicted ultrafiltration platinum in plasma or SN-38 in tumors to PD models as the driver of efficacy. The final PK/PD model can predict PK profiles and responses following treatments with oxaliplatin or irinotecan. After generation of virtual patient cohorts, this model simulated their tumor shrinkages following treatments, which were used in analyzing the efficacies of the two treatments. Consistent with the published clinical trials, the model simulation suggested similar patient responses following the treatments of oxaliplatin and irinotecan with regards to the probabilities of progression-free survival (HR = 1.05, 95%CI [0.97;1.15]) and the objective response rate (OR = 1.15, 95%CI [1.00;1.32]). This proposed translational PK/PD modeling approach provides a significant tool for predicting clinical responses of different agents, which may help decision-making in drug development and guide clinical trial design.

Toxicology Certification: The Need, the Value, and an Evolving Landscape

Certification in toxicology remains a subject of interest to those in the field, as evidenced by the number of presentations at major meetings and publications in the past decade. In 2009, Brock and colleagues summarized the certifications available in the field of toxicology and provided an international perspective of the pros and cons of gaining certification. Though that article has been viewed thousands of times, the certification processes have evolved over the past decade. Therefore, the purpose of the article is to build upon the prior work and present current information for toxicologists seeking certification with the American Board of Toxicology (DABT), Academy of Toxicological Sciences (ATS), as a European Registered Toxicologist (ERT), as a Diplomate, Japanese Society of Toxicology (DJSOT), and as a Diplomate of the Chinese Society of Toxicology (DCST). In addition, other certifications are presented, but available details are limited.

Methods for Heart Rate Variability Biofeedback (HRVB): A Systematic Review and Guidelines

Heart Rate Variability Biofeedback (HRVB) has been widely used to improve cardiovascular health and well-being. HRVB is based on breathing at an individual's resonance frequency, which stimulates respiratory sinus arrhythmia (RSA) and the baroreflex. There is, however, no methodological consensus on how to apply HRVB, while details about the protocol used are often not well reported. Thus, the objectives of this systematic review are to describe the different HRVB protocols and detect methodological concerns. PsycINFO, CINALH, Medline and Web of Science were searched between 2000 and April 2021. Data extraction and quality assessment were based on PRISMA guidelines. A total of 143 studies were finally included from any scientific field and any type of sample. Three protocols for HRVB were found: (i) "Optimal RF" (n = 37), each participant breathes at their previously detected RF; (ii) "Individual RF" (n = 48), each participant follows a biofeedback device that shows the optimal breathing rate based on cardiovascular data in real time, and (iii) "Preset-pace RF" (n = 51), all participants breathe at the same rate rate, usually 6 breaths/minute. In addition, we found several methodological differences for applying HRVB in terms of number of weeks, duration of breathing or combination of laboratory and home sessions. Remarkably, almost 2/3 of the studies did not report enough information to replicate the HRVB protocol in terms of breathing duration, inhalation/exhalation ratio, breathing control or body position. Methodological guidelines and a checklist are proposed to enhance the methodological quality of future HRVB studies and increase the information reported.

Clinical development of new drugs for adults and children with cancer, 2010-2020

BACKGROUND

Many new molecular entities enter clinical development to evaluate potential therapeutic benefits for oncology patients. We characterized adult and pediatric development of the set of new molecular entities that started clinical testing in 2010-2015 worldwide.

METHODS

We extracted data from AdisInsight, an extensive database of global pharmaceutical development, and the FDA.gov website. We followed the cohort of new molecular entities initiating first-in-human phase I clinical trials in 2010-2015 to the end of 2020. For each new molecular entity, we determined whether it was granted US Food and Drug Administration (FDA) approval, studied in a trial open to pediatric enrollment, or stalled during development. We characterized the cumulative incidence of these endpoints using statistical methods for censored data.

RESULTS

The 572 new molecular entities starting first-in-human studies in 2010-2015 were studied in 6142 trials by the end of 2020. Most new molecular entities were small molecules (n = 316, 55.2%), antibodies (n = 148, 25.9%), or antibody-drug conjugates (n = 44, 7.7%). After a mean follow-up of 8.0 years, 173 new molecular entities did not advance beyond first-in-human trials, and 39 were approved by the FDA. New molecular entities had a 10.4% estimated probability (95% confidence interval = 6.6% to 14.1%) of being approved by the FDA within 10 years of first-in-human trials. After a median of 4.6 years since start of first-in-human trials, 67 (11.7%) new molecular entities were tested in trials open to pediatric patients, and 5 (0.9%) were approved for pediatric indications.

CONCLUSIONS

More efficient clinical development strategies are needed to evaluate new cancer therapies, especially for children, and incorporate approaches to ensure knowledge gain from investigational products that stall in development.

Advancing AI in healthcare: A comprehensive review of best practices

Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools shaping the healthcare sector. This review considers twelve key aspects of AI in clinical practice: 1) Ethical AI; 2) Explainable AI; 3) Health Equity and Bias in AI; 4) Sponsorship Bias; 5) Data Privacy; 6) Genomics and Privacy; 7) Insufficient Sample Size and Self-Serving Bias; 8) Bridging the Gap Between Training Datasets and Real-World Scenarios; 9) Open Source and Collaborative Development; 10) Dataset Bias and Synthetic Data; 11) Measurement Bias; 12) Reproducibility in AI Research. These categories represent both the challenges and opportunities of AI implementation in healthcare. While AI holds significant potential for improving patient care, it also presents risks and challenges, such as ensuring privacy, combating bias, and maintaining transparency and ethics. The review underscores the necessity of developing comprehensive best practices for healthcare organizations and fostering a diverse dialogue involving data scientists, clinicians, patient advocates, ethicists, economists, and policymakers. We are at the precipice of significant transformation in healthcare powered by AI. By continuing to reassess and refine our approach, we can ensure that AI is implemented responsibly and ethically, maximizing its benefit to patient care and public health.

The 3Rs in Experimental Liver Disease

Patients with cirrhosis present multiple physiological and immunological alterations that play a very important role in the development of clinically relevant secondary complications to the disease. Experimentation in animal models is essential to understand the pathogenesis of human diseases and, considering the high prevalence of liver disease worldwide, to understand the pathophysiology of disease progression and the molecular pathways involved, due to the complexity of the liver as an organ and its relationship with the rest of the organism. However, today there is a growing awareness about the sensitivity and suffering of animals, causing opposition to animal research among a minority in society and some scientists, but also about the attention to the welfare of laboratory animals since this has been built into regulations in most nations that conduct animal research. In 1959, Russell and Burch published the book "The Principles of Humane Experimental Technique", proposing that in those experiments where animals were necessary, everything possible should be done to try to replace them with non-sentient alternatives, to reduce to a minimum their number, and to refine experiments that are essential so that they caused the least amount of pain and distress. In this review, a comprehensive summary of the most widely used techniques to replace, reduce, and refine in experimental liver research is offered, to assess the advantages and weaknesses of available experimental liver disease models for researchers who are planning to perform animal studies in the near future.

Analytical validation and quality control/quality assurance practices for improved rigor and reproducibility of biochemical assays in orthopaedic research

Orthopaedic research, and biomedical research in general, has made enormous strides to develop treatments for conditions long thought to be inevitable or untreatable; however, there is growing concern about the quality of published research. Considerable efforts have been made to improve overall research quality, integrity, and rigor, including meaningful proposals focused on transparency of reporting, appropriate use of statistics, and reporting of negative results. However, we believe that there is another key component to rigor and reproducibility that is not discussed sufficiently-analytical validation and quality control (QC). In this commentary, we discuss QC and method validation principles and practices that are systematically applied in the clinical laboratory setting to verify and monitor the analytical performance of quantitative assays, and the utility of applying similar practices to biochemical assays in the orthopaedic research setting. This commentary includes (1) recommendations for validation and QC practices, including examples of assay performance limitations uncovered by validation experiments performed in our laboratory, and (2) a description of an ongoing QC program developed to monitor the ongoing performance of commonly used assays in our lab. We hope that this commentary and the examples presented here will be thought-provoking and inspire further discussion and adaptation of analytical validation and QC procedures to advance our shared pursuit of high-quality, rigorous, and reproducible orthopaedic research.

Open Science Practices in Communication Sciences and Disorders: A Survey

PURPOSE

Open science is a collection of practices that seek to improve the accessibility, transparency, and replicability of science. Although these practices have garnered interest in related fields, it remains unclear whether open science practices have been adopted in the field of communication sciences and disorders (CSD). This study aimed to survey the knowledge, implementation, and perceived benefits and barriers of open science practices in CSD.

METHOD

An online survey was disseminated to researchers in the United States actively engaged in CSD research. Four-core open science practices were examined: preregistration, self-archiving, gold open access, and open data. Data were analyzed using descriptive statistics and regression models.

RESULTS

Two hundred twenty-two participants met the inclusion criteria. Most participants were doctoral students (38%) or assistant professors (24%) at R1 institutions (58%). Participants reported low knowledge of preregistration and gold open access. There was, however, a high level of desire to learn more for all practices. Implementation of open science practices was also low, most notably for preregistration, gold open access, and open data (< 25%). Predictors of knowledge and participation, as well as perceived barriers to implementation, are discussed.

CONCLUSION

Although participation in open science appears low in the field of CSD, participants expressed a strong desire to learn more in order to engage in these practices in the future. Supplemental Material and Open Science Form: https://doi.org/10.23641/asha.21569040.

Rewiring Drug Research and Development through Human Data-Driven Discovery (HD3)

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

Predicting and reasoning about replicability using structured groups

This paper explores judgements about the replicability of social and behavioural sciences research and what drives those judgements. Using a mixed methods approach, it draws on qualitative and quantitative data elicited from groups using a structured approach called the IDEA protocol ('investigate', 'discuss', 'estimate' and 'aggregate'). Five groups of five people with relevant domain expertise evaluated 25 research claims that were subject to at least one replication study. Participants assessed the probability that each of the 25 research claims would replicate (i.e. that a replication study would find a statistically significant result in the same direction as the original study) and described the reasoning behind those judgements. We quantitatively analysed possible correlates of predictive accuracy, including self-rated expertise and updating of judgements after feedback and discussion. We qualitatively analysed the reasoning data to explore the cues, heuristics and patterns of reasoning used by participants. Participants achieved 84% classification accuracy in predicting replicability. Those who engaged in a greater breadth of reasoning provided more accurate replicability judgements. Some reasons were more commonly invoked by more accurate participants, such as 'effect size' and 'reputation' (e.g. of the field of research). There was also some evidence of a relationship between statistical literacy and accuracy.

AI for life: Trends in artificial intelligence for biotechnology

Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.

Multifunctional targetable liposomal drug delivery system in the management of leukemia: Potential, opportunities, and emerging strategies

The concern impeding the success of chemotherapy in leukemia treatment is descending efficacy of drugs because of multiple drug resistance (MDR). The previous failure of traditional treatment methods is primarily responsible for the present era of innovative agents to treat leukemia effectively. The treatment option is a chemotherapeutic agent in most available treatment strategies, which unfortunately leads to high unavoidable toxicities. As a result of the recent surge in marketed products, theranostic nanoparticles, i.e., multifunctional targetable liposomes (MFTL), have been approved for improved and more successful leukemia treatment that blends therapeutic and diagnostic characteristics. Since they broadly offer the required characteristics to get past the traditional/previous limitations, such as the absence of site-specific anti-cancer therapeutic delivery and ongoing real-time surveillance of the leukemia target sites while administering therapeutic activities. To prepare MFTL, suitable targeting ligands or tumor-specific antibodies are required to attach to the surface of the liposomes. This review exhaustively covered and summarized the liposomal-based formulation in leukemia treatment, emphasizing leukemia types; regulatory considerations, patents, and clinical portfolios to overcome clinical translation hurdles have all been explored.

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Dose prescription and reporting in stereotactic body radiotherapy: A multi-institutional study

BACKGROUND AND PURPOSE

Radiation dose prescriptions are foundational for optimizing treatment efficacy and limiting treatment-related toxicity. We sought to assess the lack of standardization of SBRT dose prescriptions across institutions.

MATERIALS & METHODS

Dosimetric data from 1298 patients from 9 academic institutions treated with IMRT and VMAT were collected. Dose parameters D100, D98, D95, D50, and D2 were used to assess dosimetric variability.

RESULTS

Disease sites included lung (48.3 %) followed by liver (29.7 %), prostate (7.5 %), spine (6.8 %), brain (4.1 %), and pancreas (2.5 %). The PTV volume in lung varied widely with bimodality into two main groups (22.0-28.7 cm3) and (48.0-67.1 cm3). A hot spot ranging from 120-150 % was noted in nearly half of the patients, with significant variation across institutions. A D50 ≥ 110 % was found in nearly half of the institutions. There was significant dosimetric variation across institutions.

CONCLUSIONS

The SBRT prescriptions in the literature or in treatment guidelines currently lack nuance and hence there is significant variation in dose prescriptions across academic institutions. These findings add greater importance to the identification of dose parameters associated with improved clinical outcome comparisons as we move towards more hypofractionated treatments. There is a need for standardized reporting to help institutions in adapting treatment protocols based on the outcome of clinical trials. Dosimetric parameters are subsequently needed for uniformity and thereby standardizing planning guidelines to maximize efficacy, mitigate toxicity, and reduce treatment disparities are urgently needed.

Construction of multiple concentration gradients for single-cell level drug screening

Isolation and manipulation of single cells play a crucial role in drug screening. However, previously reported single-cell drug screening lacked multiple-dose concentration gradient studies, which limits their ability to predict drug performance accurately. To solve this problem, we constructed a multiconcentration gradient generator in which a Tai Chi-spiral mixer can accelerate solution mixing in a short time and produce a linear concentration gradient. Later, a gradient generator combined with a single-cell capture array was adopted to investigate the effects of single or combined doses of 5-fluorouracil and cisplatin on human hepatoma cells and human breast carcinoma cells (at the single-cell level). The results showed that both drugs were effective in inhibiting the growth of cancer cells, and the combination was more effective for human hepatoma cells. In addition, the relationship between the biomechanical heterogeneity (e.g., deformability and size) of tumor cells and potential drug resistance at the single-cell level was investigated, indicating that small and/or deformable cells were more resistant than large and/or less deformable cells. The device provides a simple and reliable platform for studying the optimal dosage of different drug candidates at the single-cell level and effectively screening single-agent chemotherapy regimens and combination therapies.

Preclinical models for drug discovery for metastatic disease

Despite many advances, metastatic disease remains essentially uncurable. Thus, there is an urgent need to better understand mechanisms that promote metastasis, drive tumor evolution, and underlie innate and acquired drug resistance. Sophisticated preclinical models that recapitulate the complex tumor ecosystem are key to this process. We begin with syngeneic and patient-derived mouse models that are the backbone of most preclinical studies. Second, we present some unique advantages of fish and fly models. Third, we consider the strengths of 3D culture models for resolving remaining knowledge gaps. Finally, we provide vignettes on multiplexed technologies to advance our understanding of metastatic disease.

Pursuitworthiness in urgent research: Lessons on well-ordered science from sustainability science

Urgent and complex sustainability problems are redefining the conditions for success of science and instigating novel methodologies and new roles of values in science. At the same time, sustainability research, mostly under the umbrella of sustainability science, is abundant with dubious methods and dubious aims of research, contributing to the already widespread crisis of the quality control mechanisms in science. This paper identifies some of these dubious methods (non-systemic thinking and specific contractual funding mechanisms) and dubious aims (unclear aims and undisclosed value assumptions), and argues that, with proper expert evaluation, it is possible to predict the type of content (and the scientific value) these forms of research will result in. Identifying the forms of research with predictably dubious results has practical implications for doing and evaluating research in sustainability science, on the one hand, while on the other, it contributes to the discussion on the ideal of well-ordered science by concretizing the ideal and developing a criterion for ordering science in the case of sustainability science. Finally, the paper provides a connection of sustainability science with the meta-science discussions on the crisis in the quality of science and on science organization, what at the same time more closely connects the philosophy of science with problems encountered in research practice tackling urgent, complex and value-laden problems.

Between therapy effect and false-positive result in animal experimentation

Despite the animal models' complexity, researchers tend to reduce the number of animals in experiments for expenses and ethical concerns. This tendency makes the risk of false-positive results, as statistical significance, the primary criterion to validate findings, often fails if testing small samples. This study aims to highlight such risks using an example from experimental regenerative therapy and propose a machine-learning solution to validate treatment effects. The example analysed was the pharmacological treatment of ear pinna punch wound healing in mice. Wound closure data analysed included eight groups treated with an epigenetic inhibitor, zebularine, and eight control groups receiving vehicle alone, of six mice each. We confirmed the zebularine healing effect for all 64 pairwise comparisons between treatment and control groups but also determined minor yet statistically significant differences between control groups in five of 28 possible comparisons. The occurrences of significant differences between the control groups, regardless of standardised experimental conditions, indicate a risk of statistically significant effects in the case a compound lacking the desired biological activity is tested. Since the criterion of statistical significance itself can be confusing, we demonstrate a machine-learning algorithm trained on datasets representing treatment and control experiments as a helpful tool for validating treatment outcomes. We tested two machine-learning approaches, Naïve Bayes and Support Vector Machine classifiers. In contrast to the Mann-Whitney U-test, indicating enhanced healing effects for some control groups receiving saline alone, both machine-learning algorithms faultlessly assigned all animal groups receiving saline to the controls.

Spatiotemporal dissection of tumor microenvironment via in situ sensing and monitoring in tumor-on-a-chip

Real-time monitoring in the tumor microenvironment provides critical insights of cancer progression and mechanistic understanding of responses to cancer treatments. However, clinical challenges and significant questions remain regarding assessment of limited clinical tissue samples, establishment of validated, controllable pre-clinical cancer models, monitoring of static versus dynamic markers, and the translation of insights gained from in vitro tumor microenvironments to systematic investigation and understanding in clinical practice. State-of-art tumor-on-a-chip strategies will be reviewed herein, and emerging real-time sensing and monitoring platforms for on-chip analysis of tumor microenvironment will also be examined. The integration of the sensors with tumor-on-a-chip platforms to provide spatiotemporal information of the tumor microenvironment and the associated challenges will be further evaluated. Though optimal integrated systems for in situ monitoring are still in evolution, great promises lie ahead that will open new paradigm for rapid, comprehensive analysis of cancer development and assist clinicians with powerful tools to guide the diagnosis, prognosis and treatment course in cancer.