Preclinical Drug Efficacy Studies Research Articles

A proof-of-concept assay for quantitative and optical assessment of drug-induced toxicity in renal organoids

Kidneys are complex organs, and reproducing their function and physiology in a laboratory setting remains difficult. During drug development, potential compounds may exhibit unexpected nephrotoxic effects, which imposes a significant financial burden on pharmaceutical companies. As a result, there is an ongoing need for more accurate model systems. The use of renal organoids to simulate responses to nephrotoxic insults has the potential to bridge the gap between preclinical drug efficacy studies in cell cultures and animal models, and the stages of clinical trials in humans. Here we established an accessible fluorescent whole-mount approach for nuclear and membrane staining to first provide an overview of the organoid histology. Furthermore, we investigated the potential of renal organoids to model responses to drug toxicity. For this purpose, organoids were treated with the chemotherapeutic agent doxorubicin for 48 h. When cell viability was assessed biochemically, the organoids demonstrated a significant, dose-dependent decline in response to the treatment. Confocal microscopy revealed visible tubular disintegration and a loss of cellular boundaries at high drug concentrations. This observation was further reinforced by a dose-dependent decrease of the nuclear area in the analyzed images. In contrast to other approaches, in this study, we provide a straightforward experimental framework for drug toxicity assessment in renal organoids that may be used in early research stages to assist screen for potential adverse effects of compounds.

Culture-Free Enumeration of Mycobacterium tuberculosis in Mouse Tissues Using the Molecular Bacterial Load Assay for Preclinical Drug Development.

Background: The turnaround times for phenotypic tests used to monitor the bacterial load of Mycobacterium tuberculosis, in both clinical and preclinical studies, are delayed by the organism’s slow growth in culture media. The existence of differentially culturable populations of M. tuberculosis may result in an underestimate of the true number. Moreover, culture methods are susceptible to contamination resulting in loss of critical data points. Objectives: We report the adaptation of our robust, culture-free assay utilising 16S ribosomal RNA, developed for sputum, to enumerate the number of bacteria present in animal tissues as a tool to improve the read-outs in preclinical drug efficacy studies. Methods: Initial assay adaptation was performed using naïve mouse lungs spiked with known quantities of M. tuberculosis and an internal RNA control. Tissues were homogenised, total RNA extracted, and enumeration performed using RT-qPCR. We then evaluated the utility of the assay, in comparison to bacterial counts estimated using growth assays on solid and liquid media, to accurately inform bacterial load in tissues from M. tuberculosis-infected mice before and during treatment with a panel of drug combinations. Results: When tested on lung tissues derived from infected mice, the MBL assay produced comparable results to the bacterial counts in solid culture (colony forming units: CFU). Notably, under specific drug treatments, the MBL assay was able to detect a significantly higher number of M. tuberculosis compared to CFU, likely indicating the presence of bacteria that were unable to produce colonies in solid-based culture. Additionally, growth recovery in liquid media using the most probable number (MPN) assay was able to account for the discrepancy between the MBL assay and CFU number, suggesting that the MBL assay detects differentially culturable sub-populations of M. tuberculosis. Conclusions: The MBL assay can enumerate the bacterial load in animal tissues in real time without the need to wait for extended periods for cultures to grow. The readout correlates well with CFUs. Importantly, we have shown that the MBL is able to measure specific populations of bacteria not cultured on solid agar. The adaptation of this assay for preclinical studies has the potential to decrease the readout time of data acquisition from animal experiments and could represent a valuable tool for tuberculosis drug discovery and development.

TB-9 An attempt to establish a patient-derived brain tumor culture model by organoid culture method

Background: Molecular heterogeneity among and within tumors are one of the reasons for the poor survival rate of brain tumors even with the current standard therapy. However, monolayer culture and neuro-sphere culture (NS) use exogenous growth factors, so may not show the true nature of the tumor. And the culture establishment rate is low, especially low-grade tumors. Therefore, we used the glioblastoma organoid (GBO) culture method showed by Fadi to create culture models of various brain tumors and investigated their characteristics. Methods: We examined the establishment rate in pathological and genotypic types of 56 patients who underwent brain tumor resection at our hospital between January 2020 and June 2021 and were cultured with GBO or NS. If tumor cells are increased visually at 1 month after culture, we defined establishment. Results: There were 15 cases of glioblastoma, 7 cases of anaplastic astrocytoma, 7 cases of diffuse astrocytoma, 3 cases of diffuse midline glioma, 2 cases of anaplastic oligodendroglioma, 5 cases of oligodendroglioma, and 16 cases of others. The establishment rate was 76.5% by the GBO method and 40% by the N S method. By histological type, GBO: 80% in glioblastoma, NS: 58.3% in glioblastoma, GBO: 83.3% in AA, NS: 40% in AA, and GBO: 100% in DA. The IDH mutation and pTERT mutation were investigated in GBO: IDHwt/TERT+ 87.5%, IDHwt/TERT- 64.3%, IDHmt/TERT- 100%, and in NS: IDHwt/TERT+ 75%, IDHwt/TERT- 33.3%, IDHmt/ TERT- 20% in NS. In addition, establishment was observed in GBO 2 case in medulloblastoma, 1 case in ependymoma. Discussion and Conclusion: This suggest that GBO can be used to establish culture models for low-grade tumors. In addition, GBO can establish culture earlier, so it is expected to be applicable to personalized therapies such as preclinical drug efficacy studies tailored to individual patients.

MAPK-Erk1/2 Pathway Activation and Survival Of Chronic Lymphocytic Leukemia (CLL) Cells Is Induced By In Vitro Modeling Of The Tumor Microenvironment and Can Be Effectively Inhibited Using The MEK1 Inhibitor MEK162

Infiltration of chronic lymphocytic leukemia (CLL) cells into the lymph node and bone marrow microenvironments promotes leukemic cell survival, proliferation and ultimately drives disease progression. Successful treatment of CLL relies on targeting the cells within these niches.Activity of the MAPK-Erk1/2 pathway is considered to be crucial for the survival of a diverse range of cancer cell types, including CLL cells. However, to date pre-clinical studies of inhibitors of MEK1/2, a key component in this pathway, have demonstrated little efficacy against CLL cells.We have recently shown that MAPK-Erk1/2 pathway activity is attenuated during ex vivo cell processing and subsequent culture of primary CLL cells; an observation which suggests that factors exist in vivo that maintain Erk1/2 activity and promote cell survival.Here we present data that show that in vitro modeling of the effects of the microenvironment on primary CLL cells, using recombinant stroma-derived factor CXCL12 or media conditioned by the HS5 stromal cell line (HS5-CM), results in a significant increase in MAPK-Erk1/2 pathway activity. Treatment with the MEK1/2 inhibitor MEK162 (Novartis) blocked the effects of CXCL12 and HS5-CM on Erk1/2 activation in CLL cells. In the presence of CXCL12 or HS5-CM, MEK162 induced apoptosis in a significantly (P< 0.05) greater proportion of cells compared to treatment of cells cultured in media alone (media: 30.14 ± 5.74%, CXCL12: 78.34 ± 9.06%, HS5 CM: 65.79 ± 12.88%). Furthermore, MEK162 completely abrogated the survival advantage conferred by the presence of HS5-CM and reduced cell migration through a permeable support towards HS5-CM by an average of 35.64 +/- 8.48%.In conclusion, our work suggests that MAPK-Erk1/2 signaling is abrogated in CLL cells during ex vivo processing and subsequent culture. This observation provides a rational explanation for the current lack of in vitro data concerning any efficacy of MEK inhibitors against CLL cells. Our data show that by introducing purified stroma-derived factor (SDF-1a/CXCL12) or media conditioned by the HS5 stromal cell line it is possible to recapitulate the activity of Erk1/2 and increase CLL-cell sensitivity to the MEK1/2 inhibitor MEK162. These observations highlight the importance of modeling the influence of the microenvironment in all pre-clinical drug-efficacy studies in CLL and suggest that inhibition of MEK1/2 may represent a novel treatment strategy for CLL. Disclosures:No relevant conflicts of interest to declare.

Hydroxychloroquine prophylaxis and treatment is ineffective in macaque and hamster SARS-CoV-2 disease models.

We remain largely without effective prophylactic/therapeutic interventions for COVID-19. Although many human COVID-19 clinical trials are ongoing, there remains a deficiency of supportive preclinical drug efficacy studies to help guide decisions. Here we assessed the prophylactic/therapeutic efficacy of hydroxychloroquine (HCQ), a drug of interest for COVID-19 management, in 2 animal disease models. The standard human malaria HCQ prophylaxis (6.5 mg/kg given weekly) and treatment (6.5 mg/kg given daily) did not significantly benefit clinical outcome, nor did it reduce SARS-CoV-2 replication/shedding in the upper and lower respiratory tract in the rhesus macaque disease model. Similarly, when used for prophylaxis or treatment, neither the standard human malaria dose (6.5 mg/kg) nor a high dose (50 mg/kg) of HCQ had any beneficial effect on clinical disease or SARS-CoV-2 kinetics (replication/shedding) in the Syrian hamster disease model. Results from these 2 preclinical animal models may prove helpful in guiding clinical use of HCQ for prophylaxis/treatment of COVID-19.

Refinement and validation of infrared thermal imaging (IRT): a non-invasive technique to measure disease activity in a mouse model of rheumatoid arthritis

BackgroundThe discovery and development of new medicines requires high-throughput screening of possible therapeutics in a specific model of the disease. Infrared thermal imaging (IRT) is a modern assessment method with extensive clinical and preclinical applications. Employing IRT in longitudinal preclinical setting to monitor arthritis onset, disease activity and therapeutic efficacies requires a standardized framework to provide reproducible quantitative data as a precondition for clinical studies.MethodsHere, we established the accuracy and reliability of an inexpensive smartphone connected infrared (IR) camera against known temperature objects as well as certified blackbody calibration equipment. An easy to use protocol incorporating contactless image acquisition and computer-assisted data analysis was developed to detect disease-related temperature changes in a collagen-induced arthritis (CIA) mouse model and validated by comparison with two conventional methods, clinical arthritis scoring and paw thickness measurement. We implemented IRT to demonstrate the beneficial therapeutic effect of nanoparticle drug delivery versus free methotrexate (MTX) in vivo.ResultsThe calibrations revealed high accuracy and reliability of the IR camera for detecting temperature changes in the rheumatoid arthritis animal model. Significant positive correlation was found between temperature changes and paw thickness measurements as the disease progressed. IRT was found to be superior over the conventional techniques specially at early arthritis onset, when it is difficult to observe subclinical signs and measure structural changes.ConclusionIRT proved to be a valid and unbiased method to detect temperature changes and quantify the degree of inflammation in a rapid and reproducible manner in longitudinal preclinical drug efficacy studies.

Fluorescent multiplexing of 3D spheroids: Analysis of biomarkers using automated immunohistochemistry staining platform and multispectral imaging.

In preclinical cancer studies, three‐dimensional (3D) cell spheroids and aggregates are preferred over monolayer cell cultures due to their architectural and functional similarity to solid tumors. We performed a proof‐of‐concept study to generate physiologically relevant and predictive preclinical models using non–small cell lung adenocarcinoma, and colon and colorectal adenocarcinoma cell line‐derived 3D spheroids and aggregates. Distinct panels were designed to determine the expression profiles of frequently studied biomarkers of the two cancer subtypes. The lung adenocarcinoma panel included ALK, EGFR, TTF‐1, and CK7 biomarkers, and the colon and colorectal adenocarcinoma panel included BRAF V600E, MSH2, MSH6, and CK20. Recent advances in immunofluorescence (IF) multiplexing and imaging technology enable simultaneous detection and quantification of multiple biomarkers on a single slide. In this study, we performed IF staining of multiple biomarkers per section on formalin‐fixed paraffin‐embedded 3D spheroids and aggregates. We optimized protocol parameters for automated IF and demonstrated staining concordance with automated chromogenic immunohistochemistry performed with validated protocols. Next, post‐acquisition spectral unmixing of the captured fluorescent signals were utilized to delineate four differently stained biomarkers within a single multiplex IF image, followed by automated quantification of the expressed markers. This workflow has the potential to be adapted to preclinical high‐throughput screening and drug efficacy studies utilizing 3D spheroids from cancer cell lines and patient‐derived organoids. The process allows for cost, time, and resource savings through concurrent staining of several biomarkers on a single slide, the ability to study the interactions of multiple expressed proteins within a single region of interest, and enable quantitative assessment of biomarkers in cancer cells.

Integrative analyses of signaling and DNA damage repair pathways in patient-derived xenograft (PDX) models from NCI’s patient-derived models repository (PDMR).

3111 Background: Patient-derived xenografts (PDXs) are increasingly being used in translational cancer research for preclinical drug efficacy studies. The National Cancer Institute (NCI) has developed a Patient-Derived Models Repository (NCI PDMR; pdmr.cancer.gov ) of PDXs with clinical annotation, proteomics, and comprehensive genomic datasets to facilitate these studies. Here, we present an integrative genomic, transcriptomic, and proteomic analysis of critical signaling and DNA damage repair pathways in these PDX models, which represent 9 common and multiple rare tumor histologies. Methods: 304 PDX models from 294 patients were established from various solid tumor histologies from patients with primary or metastatic cancer. Whole Exome Sequencing, RNA-Seq and Reverse Phase Protein Array (RPPA) were performed on 2-9 PDXs per model across multiple passages. An integrative workflow was applied on multiple data sets to detect pathway activation. Results: We profiled 10 signaling and 5 DNA repair pathways in the PDMR dataset. We observed that: (i) a large fraction (40%) of PDX models have at least 1 targetable mutation in the RTK/RAS and/or PIK3CA pathways; (ii) 131 models (45%) have putative driver and oncogenic mutations and copy number variants (CNVs) in the WNT, TGFRb , NRF2 and NOTCH pathways. In addition, 17% of PDX models have targetable mutations in DNA damage repair pathways and 20 PDMR models have a DNA mismatch repair defect (MSI-H). We confirmed activation of the signaling pathways in a subset of PDX models by pathway enrichment analysis on gene expression data from RNASeq and phosphoprotein-specific antibody binding data from RPPA. Activation of DNA repair processes was confirmed by enrichment of relevant mutational signatures and loss of heterozygosity in these PDX models. Conclusions: Genomic analysis of NCI PDMR models revealed that a large fraction have clinically relevant somatic alterations in key signaling and DNA damage repair pathways. Further integrative analyses with matched transcriptomic and proteomic profiles confirmed pathway activation in a subset of these models, which may prioritize them for preclinical drug studies.

Signaling pathway screening platforms are an efficient approach to identify therapeutic targets in cancers that lack known driver mutations: a case report for a cancer of unknown primary origin

Precision medicine aims to tailor cancer therapies to target specific tumor-promoting aberrations. For tumors that lack actionable drivers, which occurs frequently in the clinic, extensive molecular characterization and pre-clinical drug efficacy studies will be required. A cell line maintained at low passage and a patient- derived xenograft model (PDX) were generated using a fresh biopsy from a patient with a poorly-differentiated neuroendocrine tumor of unknown primary origin. Next-generation sequencing, high throughput signaling network analysis, and drug efficacy trials were then conducted to identify actionable targets for therapeutic intervention. No actionable mutations were identified after whole exome sequencing of the patient’s DNA. However, whole genome sequencing revealed amplification of the 3q and 5p chromosomal arms, that include the PIK3CA and RICTOR genes, respectively. We then conducted pathway analysis, which revealed activation of the AKT pathway. Based on this analysis, efficacy of PIK3CA and AKT inhibitors were evaluated in the tumor biopsy-derived cell culture and PDX, and response to the AKT inhibitor AZD5363 was observed both in vitro and in vivo indicating the patient would benefit from targeted therapies directed against the serine/threonine kinase AKT. In conclusion, our study demonstrates that high throughput signaling pathway analysis will significantly aid in identifying actionable alterations in rare tumors and guide patient stratification into early-phase clinical trials.

Abstract A185: Combining unbiased network analysis and cancer genomics to guide precision medicine-oriented early-phase clinical trials

Abstract Background: Precision medicine aims to tailor cancer therapies to target specific tumor-promoting aberrations. For tumors that lack actionable drivers, extensive molecular characterization and preclinical drug efficacy studies will be required to match a patient with the appropriate targeted therapy. Patients and Methods: Next-generation sequencing, high-throughput signaling network analysis, and drug efficacy trials were used in a tumor-derived cell line and a patient-derived xenograft (PDX) model to identify actionable targets for therapeutic intervention. Results: A patient-derived xenograft (PDX) model for preclinical research was successfully established and no actionable mutations were identified after whole exome sequencing of the patient’s DNA. Whole genome sequencing revealed amplification of the 3q and 5p chromosomal arms, which include the PIK3CA and RICTOR genes, respectively. Consistent with amplification of these genes, pathway analysis revealed activation of the AKT pathway. Based on this analysis, efficacy of PIK3CA and AKT inhibitors were evaluated, and response to the AKT inhibitor AZD5363 was observed in in vitro and in vivo models established from the patient’s biopsy, indicating the patient would benefit from targeted therapies directed against the serine/threonine kinase AKT. Conclusion: High-throughput signaling pathway analysis complements next-generation sequencing approaches for detection of actionable alterations and will aid in patient stratification into early-phase clinical trials. Citation Format: Pedro Torres-Ayuso, Sudhakar Sahoo, Christopher Chester, Cassandra Hodgkinson, Melanie Galvin, Hui Sun Leong, Kristopher Frese, Richard Marais, Caroline Dive, Matthew Krebs, John Brognard. Combining unbiased network analysis and cancer genomics to guide precision medicine-oriented early-phase clinical trials [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A185.

Generation of Pediatric Leukemia Xenograft Models in NSG-B2m Mice: Comparison with NOD/SCID Mice

Generation of orthotopic xenograft mouse models of leukemia is important to understand the mechanisms of leukemogenesis, cancer progression, its cross talk with the bone marrow microenvironment, and for preclinical evaluation of drugs. In these models, following intravenous injection, leukemic cells home to the bone marrow and proliferate there before infiltrating other organs, such as spleen, liver, and the central nervous system. Moreover, such models have been shown to accurately recapitulate the human disease and correlate with patient response to therapy and prognosis. Thus, various immune-deficient mice strains have been used with or without recipient preconditioning to increase engraftment efficiency. Mice homozygous for the severe combined immune deficiency (SCID) mutation and with non-obese diabetic background (NOD/SCID) have been used in the majority of leukemia xenograft studies. Later, NOD/SCID mice deficient for interleukin 2 receptor gamma chain (IL2Rγ) gene called NSG mice became the model of choice for leukemia xenografts. However, engraftment of leukemia cells without irradiation preconditioning still remained a challenge. In this study, we used NSG mice with null alleles for major histocompatibility complex class I beta2-microglobulin (β2m) called NSG-B2m. This is a first report describing the 100% engraftment efficiency of pediatric leukemia cell lines and primary samples in NSG-B2m mice in the absence of host preconditioning by sublethal irradiation. We also show direct comparison of the engraftment efficiency and growth rate of pediatric acute leukemia cells in NSG-B2m and NOD/SCID mice, which showed 80–90% engraftment efficiency. Secondary and tertiary xenografts in NSG-B2m mice generated by injection of cells isolated from the spleens of leukemia-bearing mice also behaved similar to the primary patient sample. We have successfully engrafted 25 acute lymphoblastic leukemia (ALL) and 5 acute myeloid leukemia (AML) patient samples with distinct cytogenetic characteristics in NSG-B2m mice, with the purpose of generating pediatric ALL and AML xenografts for preclinical evaluation of drugs. Thus, our data support the use of NSG-B2m mouse model for leukemia engraftment and in vivo preclinical drug efficacy studies.

Rabbit Models of Ocular Diseases: New Relevance for Classical Approaches

Over 100 million individuals are affected by irreversible visual impairments and blindness worldwide, while ocular diseases remain a challenging problem despite significant advances in modern ophthalmology. Development of novel drugs and drug delivery mechanisms, as well as advanced ophthalmological techniques requires experimental models including animals, capable of developing ocular diseases with similar etiology and pathology, suitable for future trials of new therapeutic approaches. Although experimental ophthalmology and visual research are traditionally performed on rodent models, these animals are often unsuitable for pre-clinical drug efficacy and safety studies, as well as for testing novel drug delivery approaches, e.g. controlled release of pharmaceuticals using intra-ocular implants. Therefore, rabbit models of ocular diseases are particularly useful in this context, since rabbits can be easily handled, while sharing more common anatomical and biochemical features with humans compared to rodents, including longer life span and larger eye size. This review provides a brief description of clinical, morphological and mechanistic aspects of the most common ocular diseases (dry eye syndrome, glaucoma, age-related macular degeneration, light-induced retinopathies, cataract and uveitis) and summarizes the diversity of current strategies for their experimental modeling in rabbits. Several applications of some of these models in ocular pharmacology and eye care strategies are also discussed.

Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers.

The tumor microenvironment is emerging as a key regulator of cancer growth and progression, however the exact mechanisms of interaction with the tumor are poorly understood. Whilst the majority of genomic profiling efforts thus far have focused on the tumor, here we investigate RNA-Seq as a hypothesis-free tool to generate independent tumor and stromal biomarkers, and explore tumor-stroma interactions by exploiting the human-murine compartment specificity of patient-derived xenografts (PDX).Across a pan-cancer cohort of 79 PDX models, we determine that mouse stroma can be separated into distinct clusters, each corresponding to a specific stromal cell type. This implies heterogeneous recruitment of mouse stroma to the xenograft independent of tumor type. We then generate cross-species expression networks to recapitulate a known association between tumor epithelial cells and fibroblast activation, and propose a potentially novel relationship between two hypoxia-associated genes, human MIF and mouse Ddx6. Assessment of disease subtype also reveals MMP12 as a putative stromal marker of triple-negative breast cancer. Finally, we establish that our ability to dissect recruited stroma from trans-differentiated tumor cells is crucial to identifying stem-like poor-prognosis signatures in the tumor compartment.In conclusion, RNA-Seq is a powerful, cost-effective solution to global analysis of human tumor and mouse stroma simultaneously, providing new insights into mouse stromal heterogeneity and compartment-specific disease markers that are otherwise overlooked by alternative technologies. The study represents the first comprehensive analysis of its kind across multiple PDX models, and supports adoption of the approach in pre-clinical drug efficacy studies, and compartment-specific biomarker discovery.

Automated image analysis of a glomerular injury marker desmin in spontaneously diabetic Torii rats treated with losartan.

Diabetic nephropathy is a major complication in diabetes and a leading cause of end-stage renal failure. Glomerular podocytes are functionally and structurally injured early in diabetic nephropathy. A non-obese type 2 diabetes model, the spontaneously diabetic Torii (SDT) rat, is of increasing preclinical interest because of its pathophysiological similarities to human type 2 diabetic complications including diabetic nephropathy. However, podocyte injury in SDT rat glomeruli and the effect of angiotensin II receptor blocker treatment in the early stage have not been reported in detail. Therefore, we have evaluated early stages of glomerular podocyte damage and the beneficial effect of early treatment with losartan in SDT rats using desmin as a sensitive podocyte injury marker. Moreover, we have developed an automated, computational glomerulus recognition method and illustrated its specific application for quantitatively studying glomerular desmin immunoreactivity. This state-of-the-art method enabled automatic recognition and quantification of glomerular desmin-positive areas, eliminating the need to laboriously trace glomerulus borders by hand. The image analysis method not only enabled assessment of a large number of glomeruli, but also clearly demonstrated that glomerular injury was more severe in the juxtamedullary region than in the superficial cortex region. This applied not only in SDT rat diabetic nephropathy but also in puromycin aminonucleoside-induced nephropathy, which was also studied. The proposed glomerulus image analysis method combined with desmin immunohistochemistry should facilitate evaluations in preclinical drug efficacy studies as well as elucidation of the pathophysiology of diabetic nephropathy.

Frequency Analyses Can Be Improved by a Modified t-test in Sample-based Preclinical Efficacy Studies

Sample-based preclinical drug efficacy studies compare frequency (proportion) or incidences of successes within respective samples of test and control groups. The word success in principle refers to a protected (e.g., due to vaccination), recovered, or surviving animal, depending on the particular experiment. We introduce here a modified t-test for two independent groups, aimed at statistical analysis of the difference between frequencies of successes in sample based preclinical studies. The test is applicable whenever the study is based on repeating replicate experiments, as required by certain procedures such as validation. Such experiments are based on constant drug dose and performed under identical conditions and protocol. The proposed test combines the computational rules of t-test for two independent groups and analysis of variance. In the initial steps, incidences are transformed to proportions, and variance between proportions in samples of the j(th) group (s(p(j))(2)), is then transformed into theoretical weighted variance within the i(th) repetition (sample) of the j(th) group (s(i,j)(2)). The variance of proportions in samples of the size of the whole group (SE(j)(2)) is then calculated. The t-statistic is computed according to the rules of t-test for two independent groups. Significance is calculated using (N(1) - 1) + (N(2) - 1) degrees of freedom, where N(j) denotes the total number of animals in the j(th) group. The proposed model offers an important advantage over incidence or proportion distribution models, such as chi-square or normal approximation of binomial distribution, respectively, because it considers variance between replicate experiments. It moreover offers important flexibility by limiting the requirement for identical sample sizes only to samples within the control or test group. A difference between groups in sample sizes, number of samples, or both, preventing application of block designs or the standard formats of t-test, may still exist. Theoretical considerations and working examples are provided. Sample based preclinical drug efficacy studies compare frequency (proportion) or incidences of successful results (e.g., protected, recovered, or surviving animals, depending on the particular experiment) within respective samples of the test and the control groups. Certain procedures, such as validation, require replicate experiments that are identical in all controllable factors, such as drug dose, sample sizes within each group, general experimental conditions, etc. Still, the control sample size is not required to be identical to that of the respective test sample size. In such cases, t-test or block designs are not applicable for statistical analyses. Moreover, incidence or frequency distribution models, such as chi-square or normal approximation of binomial distribution, respectively, which are performed on pooled data of the examined groups, ignore variance between experiments and thereby result in impaired validity of the statistical inference. We propose here a modified t-test that limits the requirement for identical sample sizes to only within each group. This aim is achieved by combining the computational rules of t-test together with analysis of variance. The proposed t-test allows the incorporating of variance between experiments into frequency or incidence assessments. We recommend using the proposed modified t-test as a complementary test to incidence distribution models.

“Beyond the Scope of Ordinary Training and Knowledge”: The Argument for Droit Moral, U.S. Research Science Intellectual Property Moral Rights

“Beyond the Scope of Ordinary Training and Knowledge”: The Argument for Droit Moral, U.S. Research Science Intellectual Property Moral Rights

Automated recognition and quantification of pancreatic islets in Zucker diabetic fatty rats treated with exendin-4

Type 2 diabetes is characterized by impaired insulin secretion from pancreatic β-cells. Quantification of the islet area in addition to the insulin-positive area is important for detailed understanding of pancreatic islet histopathology. Here we show computerized automatic recognition of the islets of Langerhans as a novel high-throughput method to quantify islet histopathology. We utilized state-of-the-art tissue pattern recognition software to enable automatic recognition of islets, eliminating the need to laboriously trace islet borders by hand. After training by a histologist, the software successfully recognized even irregularly shaped islets with depleted insulin immunostaining, which were quite difficult to automatically recognize. The results from automated image analysis were highly correlated with those from manual image analysis. To establish whether this automated, rapid, and objective determination of islet area will facilitate studies of islet histopathology, we showed the beneficial effect of chronic exendin-4, a glucagon-like peptide-1 analog, treatment on islet histopathology in Zucker diabetic fatty (ZDF) rats. Automated image analysis provided qualitative and quantitative evidence that exendin-4 treatment ameliorated the loss of pancreatic insulin content and gave rise to islet hypertrophy. We also showed that glucagon-positive α-cell area was decreased significantly in ZDF rat islets with disorganized structure. This study is the first to demonstrate the utility of automatic quantification of digital images to study pancreatic islet histopathology. The proposed method will facilitate evaluations in preclinical drug efficacy studies as well as elucidation of the pathophysiology of diabetes.

Treatment of Duodenal Ulcer

Sample-based preclinical drug efficacy studies compare frequency (proportion) or incidences of successes within respective samples of test and control groups. The word <i>success</i> in principle refers to a protected (e.g., due to vaccination), recovered, or surviving animal, depending on the particular experiment. We introduce here a modified t-test for two independent groups, aimed at statistical analysis of the difference between frequencies of successes in sample based preclinical studies. The test is applicable whenever the study is based on repeating replicate experiments, as required by certain procedures such as validation. Such experiments are based on constant drug dose and performed under identical conditions and protocol. The proposed test combines the computational rules of t-test for two independent groups and analysis of variance. In the initial steps, incidences are transformed to proportions, and variance <i>between</i> proportions in samples of the j^th group (<i>s˜_{p_j}</i>²), is then transformed into theoretical weighted variance <i>within</i> the i^th repetition (sample) of the j^th group (<i>s˜_i,j</i>²). The variance of proportions in samples of the size of the whole group (<i>SE_j</i>²) is then calculated. The t-statistic is computed according to the rules of t-test for two independent groups. Significance is calculated using (N₁ − 1) + (N₂ − 1) degrees of freedom, where N_j denotes the total number of animals in the j^th group. The proposed model offers an important advantage over incidence or proportion distribution models, such as chi-square or normal approximation of binomial distribution, respectively, because it considers variance between replicate experiments. It moreover offers important flexibility by limiting the requirement for identical sample sizes only to samples <i>within</i> the control or test group. A difference <i>between</i> groups in sample sizes, number of samples, or both, preventing application of block designs or the standard formats of t-test, may still exist. Theoretical considerations and working examples are provided. <i>LAY ABSTRACT:</i> Sample based preclinical drug efficacy studies compare frequency (proportion) or incidences of successful results (e.g., protected, recovered, or surviving animals, depending on the particular experiment) within respective samples of the test and the control groups. Certain procedures, such as validation, require replicate experiments that are identical in all controllable factors, such as drug dose, sample sizes <i>within</i> each group, general experimental conditions, etc. Still, the control sample size is not required to be identical to that of the respective test sample size. In such cases, t-test or block designs are not applicable for statistical analyses. Moreover, incidence or frequency distribution models, such as chi-square or normal approximation of binomial distribution, respectively, which are performed on pooled data of the examined groups, ignore variance between experiments and thereby result in impaired validity of the statistical inference. We propose here a modified t-test that limits the requirement for identical sample sizes to only <i>within</i> each group. This aim is achieved by combining the computational rules of t-test together with analysis of variance. The proposed t-test allows the incorporating of variance between experiments into frequency or incidence assessments. We recommend using the proposed modified t-test as a complementary test to incidence distribution models.