Ian Peers

Characterisation of the guinea pig model of osteoarthritis by in vivo three-dimensional magnetic resonance imaging

OBJECTIVE To characterise longitudinal changes in joint integrity and cartilage volume in vivo in the guinea pig spontaneous osteoarthritis (OA) model by magnetic resonance imaging (MRI). METHODS Guinea pigs knee were imaged in vivo by high-resolution three-dimensional (3D) MRI between the ages of 3 and 12 months. Image analysis was performed to assess qualitative knee joint changes between 3 and 12 months (n=16) and quantitative volumetric changes of the medial tibial cartilage between 9 and 12 months (n=7). After imaging, animals were killed and knees were assessed macroscopically and histologically. RESULTS From 3 to 6 months qualitative observation by MRI and histopathology indicated localised cartilage swelling on the medial tibial plateau. At 6 months, bone cysts had developed in the epiphysis. At 9 months, we observed by MRI and histopathology, fragmentation of the medial tibial cartilage in areas not protected by the meniscus. Cartilage degeneration had intensified at 12 months with evidence of widespread loss of cartilage throughout the tibial plateau. Segmentation of the MR cartilage images showed a 36% loss of volume between 9 and 12 months. CONCLUSIONS We have achieved 3D image acquisition and segmentation of knee cartilage in a guinea pig model of chronic OA, which permits measurements previously only possible in man. High resolution and short acquisition time allowed qualitative longitudinal characterisation of the entire knee joint and enabled us to quantify for the first time longitudinal tibial cartilage volume loss associated with disease progression.

In search of preclinical robustness

Systematic engagement of statisticians in preclinical research could help address the weaknesses that are undermining the likelihood of subsequent success in drug discovery and development.

Can you trust your animal study data

persists, thus increasing the risk of poor decisions and contributing to the attrition of early-stage drug projects4–7. We therefore conclude that we have an ethical and scientific obligation to improve the quality of preclinical animal studies, ensuring that where they are used, they include as few animals as possible and are run in a way that provides reproducible and valid information. We believe that a systematic approach to reviewing animal study protocols against key statistical principles supports this goal, providing a robust challenge to study design, analysis and interpretation, thereby helping to ensure the internal validity of the studies and increasing confidence in resulting decisions.

Proteomic Biomarkers for Acute Interstitial Lung Disease in Gefitinib-Treated Japanese Lung Cancer Patients

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10−25), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

Use of a Canine Model of Atopic Dermatitis to Investigate the Efficacy of a CCR4 Antagonist in Allergen-Induced Skin Inflammation in a Randomized Study

Atopic dermatitis (AD) is an inflammatory skin disease characterized by infiltration of skin homing lymphocytes into the dermis. Most of these lymphocytes express the chemokine receptor CCR4, and the frequency of blood CCR4(+) lymphocytes correlates with AD disease severity. Canine AD is a pruritic inflammatory condition that shows many features of the human disease, including CCR4 overexpression. Therefore, we tested a potent selective CCR4 antagonist in an allergen challenge model of canine AD, both clinically and histologically, to investigate whether this chemokine pathway plays a role in the inflammatory response. Using a four-period randomized cross-over study design, 14 beagles were challenged with allergen and clinically monitored. Biopsy samples were taken before and after allergen challenge. A clear reduction of clinical scores was observed with oral prednisolone (P < 0.0001) but not for the CCR4 inhibitor. A subset of the dogs (5/13) showed partial inhibition (30-49%) of the clinical signs with CCR4 inhibitor treatment, and this finding was supported by the results of histopathologic analysis of skin biopsy samples. This partial response is consistent with redundancy in chemokine pathways and highlights the need for therapies blocking multiple pathways. This study shows the utility of this canine model of AD for testing new therapeutic agents.

An adaptive design for updating the threshold value of a continuous biomarker.

Potential predictive biomarkers are often measured on a continuous scale, but in practice, a threshold value to divide the patient population into biomarker ‘positive’ and ‘negative’ is desirable. Early phase clinical trials are increasingly using biomarkers for patient selection, but at this stage, it is likely that little will be known about the relationship between the biomarker and the treatment outcome. We describe a single-arm trial design with adaptive enrichment, which can increase power to demonstrate efficacy within a patient subpopulation, the parameters of which are also estimated. Our design enables us to learn about the biomarker and optimally adjust the threshold during the study, using a combination of generalised linear modelling and Bayesian prediction. At the final analysis, a binomial exact test is carried out, allowing the hypothesis that ‘no population subset exists in which the novel treatment has a desirable response rate’ to be tested. Through extensive simulations, we are able to show increased power over fixed threshold methods in many situations without increasing the type-I error rate. We also show that estimates of the threshold, which defines the population subset, are unbiased and often more precise than those from fixed threshold studies. We provide an example of the method applied (retrospectively) to publically available data from a study of the use of tamoxifen after mastectomy by the German Breast Study Group, where progesterone receptor is the biomarker of interest.

High content screening of patient-derived cell lines highlights the potential of non-standard chemotherapeutic agents for the treatment of glioblastoma

Background Glioblastoma (GBM) is the most common primary brain malignancy in adults, yet survival outcomes remain poor. First line treatment is well established, however disease invariably recurs and improving prognosis is challenging. With the aim of personalizing therapy at recurrence, we have established a high content screening (HCS) platform to analyze the sensitivity profile of seven patient-derived cancer stem cell lines to 83 FDA-approved chemotherapy drugs, with and without irradiation. Methods Seven cancer stem cell lines were derived from patients with GBM and, along with the established cell line U87-MG, each patient-derived line was cultured in tandem in serum-free conditions as adherent monolayers and three-dimensional neurospheres. Chemotherapeutics were screened at multiple concentrations and cells double-stained to observe their effect on both cell death and proliferation. Sensitivity was classified using high-throughput algorithmic image analysis. Results Cell line specific drug responses were observed across the seven patient-derived cell lines. Few agents were seen to have radio-sensitizing effects, yet some drug classes showed a marked difference in efficacy between monolayers and neurospheres. In vivo validation of six drugs suggested that cell death readout in a three-dimensional culture scenario is a more physiologically relevant screening model and could be used effectively to assess the chemosensitivity of patient-derived GBM lines. Conclusion The study puts forward a number of non-standard chemotherapeutics that could be useful in the treatment of recurrent GBM, namely mitoxantrone, bortezomib and actinomycin D, whilst demonstrating the potential of HCS to be used for personalized treatment based on the chemosensitivity profile of patient tumor cells.

Nonclinical Safety Assessment: An Introduction for Statisticians

This chapter provides an overview of the nonclinical drug safety testing process, and the statistical challenges associated with work in this area. Whilst other chapters in this book focus on specific types of designs and analyses which you may encounter during nonclinical drug development, we provide here the context for a statistician working in nonclinical safety assessment, and seek to prepare them for some of the practical issues and decisions they are likely to face. We will describe the scope and framework for the studies run within a nonclinical safety assessment programme, and recommend when and how a statistician needs to engage to add value. We will look generally at design and analysis considerations for safety studies which, whilst not unique to this context, are particularly pertinent to this area of work. Finally we will highlight some practical considerations and industry trends. If this chapter provides you with insight that complements rather than replicates what is taught in conventional statistics courses, and inspires you to believe that statistical work in this area can be both valuable and rewarding, we will have achieved our goal.

An adaptive trial design for updating the threshold of a continuous biomarker

Potential predictive biomarkers are often measured on a continuous scale, but in practice a threshold value to divide the patient population into biomarker “positive” and “negative” is disirable. Early phase clinical trials are increasingly using biomarkers for patient selection, but at this stage it is likely that little is known about the relationship between the biomarker and the treatment outcome. We describe a Phase II trial design with adaptive enrichment, which can increase power to demonstrate efficacy within a patient subpopulation, the parameters of which are also estimated. Our design enables us to learn about the biomarker and optimally adjust the threshold during the study, using a combination of generalised linear modelling an Bayesian prediction. At the final analysis, the hypothesis that “no population subset exists in which the novel treatment has a desirable effect” is tested. Through extensive simulations, we are able to show increased power over fixed threshold methods in many situations without increased false positive rates. We also show that estimates of the threshold which defines the population subset are unbiased and often more precise than those from fixed threshold studies. We provide an example of the method applied (retrospectively) to publically available data from a study of the use of tamoxifen after mastectomy by the German Breast Study Group, where progesterone receptor is the biomarker of interest.