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The pneumococcal enzyme-linked immunosorbent assay (ELISA) measures IgG antibodies in human serum, and it is an important assay that supports licensure of pneumococcal vaccines. The immune correlate of protection, 0.35 µg/ml of IgG antibodies, was determined by the ELISA method. Pfizer has developed a new Luminex-based assay platform to replace the ELISA. These papers describe the important work of (i) validating the Luminex-based assay and (ii) bridging the immune correlate of protection (0.35 µg/ml IgG) to equivalent values reported by the Luminex platform., This article describes the results of a study designed to bridge the World Health Organization (WHO) pneumococcal enzyme-linked immunosorbent assay (ELISA) platform to the validated Luminex-based 13-plex direct immunoassay (dLIA) platform developed by Pfizer, Inc. Both assay platforms quantify serotype-specific serum IgG antibodies (in micrograms per milliliter) against an international reference standard serum. The primary goal of this study was to determine if the dLIA is a suitable replacement for the ELISA to support clinical vaccine studies that include the evaluation of immune responses to serotypes 1, 3, 4, 5, 6A, 6B, 7F, 9V, 14, 18C, 19A, 19F, and 23F. Serum samples were selected from four pivotal 13-valent pneumococcal conjugate vaccine (13vPnC; Prevnar 13) clinical trials on the basis of their serotype-specific IgG concentrations by ELISA. In these studies, subjects were immunized either with 13vPnC or with 7-valent pneumococcal conjugate vaccine (7vPnC; Prevnar). There were 1,528 of 1,574 selected samples with sufficient remaining volume for reanalysis in the dLIA. A comparison of assay results from the dLIA and ELISA platforms showed clear and robust linear quantitative relationships across all 13 serotypes. In addition, lower IgG antibody concentrations in preimmunization samples were measured in the dLIA, thus allowing better differentiation between preimmunization and low-titer postimmunization samples. Overall, the results showed that the established population-level protective threshold IgG concentration, 0.35 µg/ml of serotype-specific serum IgG antibodies, is appropriate for use for data generated using the dLIA platform developed by Pfizer, Inc., for 10 serotypes: serotypes 1, 3, 4, 6A, 7F, 9V, 14, 18C, 19F, and 23F. On the basis of the extensive bridging analyses, however, the use of dLIA cutoff values of 0.23, 0.10, and 0.12 µg/ml is recommended for serotypes 5, 6B, and 19A, respectively. This adjustment will ensure that the consistency of the established population-level protective threshold IgG concentration is maintained when switching from the ELISA to the dLIA platform. The results of this bridging study demonstrate that the 13-plex dLIA platform is a suitable replacement for the WHO reference ELISA platform. IMPORTANCE The pneumococcal enzyme-linked immunosorbent assay (ELISA) measures IgG antibodies in human serum, and it is an important assay that supports licensure of pneumococcal vaccines. The immune correlate of protection, 0.35 µg/ml of IgG antibodies, was determined by the ELISA method. Pfizer has developed a new Luminex-based assay platform to replace the ELISA. These papers describe the important work of (i) validating the Luminex-based assay and (ii) bridging the immune correlate of protection (0.35 µg/ml IgG) to equivalent values reported by the Luminex platform.

Objective Molecular medicine raised expectations for strategically targeted biologic agents in systemic lupus erythematosus (SLE), but clinical trial results have been disappointing and difficult to interpret. Most studies add investigational agents to various, often effective, standard therapy immunosuppressants used at baseline, with unknown treatment interactions. Eliminating polypharmacy in trials of active lupus remains controversial. We undertook the Biomarkers of Lupus Disease study to test withdrawal of immunosuppressants as a novel approach to rendering SLE trials interpretable. Methods In 41 patients with active, non–organ-threatening SLE flare (group A), temporary steroids were given while background immunosuppressants were withdrawn. Time to loss of disease suppression (time to disease flare) and safety were evaluated; standard therapy was immediately resumed when symptoms recurred. Immunologic impacts of standard therapy were studied at baseline by multiplex assay, enzyme-linked immunosorbent assay, and messenger RNA array in group A patients plus 62 additional patients donating a single sample (group B). Results Patients with lower or higher baseline disease activity had median times to flare of 71 or 45 days, respectively; 40 of 41 patients (98%) had disease flares by 6 months. All flares were treated and resolved within 6 weeks. No serious adverse events occurred from flare or infection. Type I interferon (IFN), Th17, and B lymphocyte stimulator pathways tracked together. Baseline immunosuppressants had distinct impacts on Th17 and B lymphocyte stimulator, depending on IFN signature. Conclusion Trials in active, non–organ-threatening SLE can safely withdraw background treatments if patients who have disease flares are designated nonresponders and returned to standard therapy. Immunologic effects of standard therapy vary between IFN-defined subsets. These findings provide a strategy for minimizing or optimizing treatment combinations in lupus trials and clinical care.

An area of unmet medical need in clinical oncology has been optimizing patient selection for a given therapeutic with the goal of getting the right drug to the right patient. Recent studies have developed preclinical approaches to identifying molecular ‘signatures of resistance’ for cytotoxic therapies and prospective validation of this strategy is ongoing in the clinic. New challenges in this setting include identifying approaches to patient selection for cytostatic compounds such as signaling pathway inhibitors and stem cell targets. Here, we discuss the biomarker methodologies developed using traditional cytotoxic drugs and how these approaches can be adapted to identify biomarkers of patient selection for novel signaling inhibitors and other novel targets. It has become increasingly clear that such biomarker discovery and validation needs to begin early and continue throughout the drug development process.

Purpose: Given their accessibility, surrogate tissues, such as peripheral blood mononuclear cells (PBMC), may provide potential predictive biomarkers in clinical pharmacogenomic studies. In leukemias and lymphomas, the prognostic value of peripheral blast expression profiles is clear; however, it is unclear whether circulating mononuclear cells of patients with solid tumors might yield profiles with similar prognostic associations. Experimental Design: In this study, we evaluated the association of expression profiles in PBMCs with clinical outcomes in patients with advanced renal cell cancer. Transcriptional patterns in PBMCs of 45 renal cell cancer patients were compared with clinical outcome data at the conclusion of a phase II study of the mTOR kinase inhibitor CCI-779 to determine whether pretreatment transcriptional patterns in PBMCs were correlated with eventual patient outcomes. Results: Unsupervised hierarchical clustering of the PBMC profiles using all expressed genes identified clusters of patients with significant differences in survival. Cox proportional hazards modeling showed that the expression levels of many PBMC transcripts were predictors for the patient outcomes of time to progression and overall survival (time to death). Supervised class prediction approaches identified multivariate expression patterns in PBMCs capable of assigning favorable outcomes of time to death and time to progression in a test set of renal cancer patients, with overall performance accuracies of 72% and 85%, respectively. Conclusions: The present study provides the first example of gene expression profiling in peripheral blood, a clinically accessible surrogate tissue, for identifying patterns of gene expression associated with higher likelihoods of positive outcome in patients with a solid tumor.

Objective Our objective was to estimate the pharmacokinetic parameters of CCI-779 and its metabolite, sirolimus, and evaluate associations of exposure parameters with safety and clinical activity. Exposure parameters were also correlated with pharmacogenomic responses in peripheral blood mononuclear cells (PBMCs). Methods In this randomized, double-blind, multicenter trial, once-weekly intravenous doses of 25, 75, or 250 mg CCI-779 were administered to patients with advanced renal cancer. Whole blood for CCI-779 and sirolimus concentrations was drawn. Population pharmacokinetic analyses yielded Bayesian-predicted exposure metrics that were correlated with severity and duration of adverse events and survival. PBMC samples taken before and after treatment were examined for pharmacogenomic responses. Ribonucleic acid samples were converted to labeled probes and hybridized to oligonucleotide arrays containing more than 12,600 human sequences. Results The final population pharmacokinetic models of CCI-779 and sirolimus included 235 and 305 observations, respectively, from 50 patients. For CCI-779, dose, single versus multiple dose, and body surface area were significant pharmacokinetic covariates. For sirolimus, dose and hematocrit were significant covariates. Age, sex, or race did not influence drug disposition. CCI-779 area under the curve correlated with adverse event severity for thrombocytopenia (P = .007), pruritus (P = .011), and hyperlipemia (P = .040). Exposure (CCI-779 cumulative area under the curve) correlated with a specific subset of gene transcripts in PBMCs following 16 weeks after therapy (P < .001, Spearman correlation). Conclusions Concentrations of CCI-779 and sirolimus were adequately described with a population model incorporating factors for dose, attenuated exposure of multiple doses, body surface area, and hematocrit. Correlations with adverse event severity and duration profiles were provided to aid in the detection of treatment-emergent effects. Pharmacogenomic profiling of PBMCs identified altered ribonucleic acid transcript expression levels that correlate with exposure. These transcripts represent potential biomarkers of CCI-779 exposure in peripheral blood. Clinical Pharmacology & Therapeutics (2005) 77, 76–89; doi: 10.1016/j.clpt.2004.08.025

Clinical development of cancer therapies is associated with attrition rates as high as 80-95%. This high attrition suggests that standard preclinical pharmacology models do not accurately reflect clinical responses. The development of more predictive preclinical models requires several considerations; the relevance of the in vivo model, the administration of test agent, and the interpretation of efficacy data. PDX are cancer models developed from the direct transfer of patient tumor tissue into immunocompromised mice. A collection of PDX models, by retaining the genetic and histologic characteristics of the patients from which they were derived, represents the complexity and heterogeneity of human cancer. To minimize the clinical attrition rates of oncology compounds, we are developing hundreds of PDX models in seven major cancer indications. The collection is being molecularly profiled by RNAseq, WES, and proteomics. Profiling has identified models with robust expression of target proteins or mutant oncogenes that are likely to respond in preclinical efficacy tests. Conversely, the PDX models may provide an understanding of resistance, for example evaluating models with good target expression that fail to respond to therapy. Patient and tumor information, if known, has been collected for each PDX model including age, sex, cancer stage and grade, diagnosis, primary or metastatic site, and prior treatments. In addition to the improvements provided by the PDX models, a preclinical paradigm shift away from treatment with maximally tolerated dose towards clinically relevant dose (CRD), taking into consideration such aspects as exposure, formulation, route and schedule, is critical when attempting to predict clinical outcome from preclinical data. Also essential is the incorporation of clinically meaningful endpoints (regression) when assessing preclinical activity. We have initiated studies on cohorts of non small cell lung and breast PDX models to predict the likely clinical efficacy of candidate compounds for clinical development and to determine the CRD for standard of care (SOC) regimens required to define the most promising Phase II/III combination therapies. Anti-tumor activities were characterized using RECIST criteria of progressive disease (PD), stable disease (SD), partial response (PR), and complete response (CR). Target expression was evaluated by RNA, proteomics and immunohistochemistry. Preliminary results demonstrate a spectrum of responses against experimental therapeutics, including Phase I ADCs and are defining the CRD required for combination treatments with SOC. Identification of the most critical parameters of PDX models predicting clinical outcome will help in validating the utility of ‘n of 1′ studies with the PDX collection, inform patient enrollment strategies, guide combination therapies, and provide insight for identifying new tumor indications. Citation Format: Edward Rosfjord, Xin Han, Danielle Leahy, Erik Upeslacis, Justin Lucas, Jonathon Golas, Andrea Hooper, Fred Immermann, Bingwen Lu, Jeremy Myers, Zhengyan Kan, James Hardwick, Eric Powell, Puja Sapra, Paul Rejto, Hans-Peter Gerber, Judy Lucas. Patient derived xenograft (PDX) models: improving predictability of experimental cancer therapies. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1469. doi:10.1158/1538-7445.AM2015-1469

Ulcerative colitis (UC) and Crohn's disease (CD) are common inflammatory bowel diseases producing intestinal inflammation and tissue damage. Although emerging evidence suggests these diseases are distinct, approximately 10% of patients remain classified as indeterminate inflammatory bowel disease even after invasive colonoscopy intended for diagnosis. A molecular diagnostic assay using a clinically accessible tissue would greatly assist in the classification of these diseases. In the present study we assessed transcriptional profiles in peripheral blood mononuclear cells from 42 healthy individuals, 59 CD patients, and 26 UC patients by hybridization to microarrays interrogating more than 22,000 sequences. Supervised analysis identified a set of 12 genes that distinguished UC and CD patient samples with high accuracy. The alterations in transcript levels observed by microarray were verified by real-time polymerase chain reaction. The results suggest that a peripheral blood mononuclear cell-based gene expression signature can provide a molecular biomarker that can complement the standard diagnosis of UC and CD.

Given their accessibility, surrogate tissues, such as peripheral blood mononuclear cells (PBMC), may provide potential predictive biomarkers in clinical pharmacogenomic studies. In leukemias and lymphomas, the prognostic value of peripheral blast expression profiles is clear; however, it is unclear whether circulating mononuclear cells of patients with solid tumors might yield profiles with similar prognostic associations.In this study, we evaluated the association of expression profiles in PBMCs with clinical outcomes in patients with advanced renal cell cancer. Transcriptional patterns in PBMCs of 45 renal cell cancer patients were compared with clinical outcome data at the conclusion of a phase II study of the mTOR kinase inhibitor CCI-779 to determine whether pretreatment transcriptional patterns in PBMCs were correlated with eventual patient outcomes.Unsupervised hierarchical clustering of the PBMC profiles using all expressed genes identified clusters of patients with significant differences in survival. Cox proportional hazards modeling showed that the expression levels of many PBMC transcripts were predictors for the patient outcomes of time to progression and overall survival (time to death). Supervised class prediction approaches identified multivariate expression patterns in PBMCs capable of assigning favorable outcomes of time to death and time to progression in a test set of renal cancer patients, with overall performance accuracies of 72% and 85%, respectively.The present study provides the first example of gene expression profiling in peripheral blood, a clinically accessible surrogate tissue, for identifying patterns of gene expression associated with higher likelihoods of positive outcome in patients with a solid tumor.

We address the problem of designing a general-purpose combinatorial library to screen for pharmaceutical leads. Conventional approaches focus on diversity as the primary factor in designing such libraries. We suggest making screening libraries out of a set of pharmaceutically relevant scaffolds, with multiple analogs per scaffold. The rationale for this rests on the fact that even though the hit-rate in active series is much higher than in the database as a whole, often a large fraction of the compounds in active series are inactive. This is especially true when the series has not been optimized for the target under study. We introduce the concept of ”hit-rate within a series“ and use historic screening data to arrive at a crude estimate for it. We then use simple probability arguments to show that 50-100 compounds are required in each series in order to be nearly certain of finding at least one active compound in each true active series for any given target.

Objective To identify cerebrospinal fluid (CSF) protein changes in persons who will develop familial Alzheimer disease (FAD) due to PSEN1 and APP mutations, using unbiased proteomics. Design We compared proteomic profiles of CSF from individuals with FAD who were mutation carriers (MCs) and related noncarriers (NCs). Abundant proteins were depleted and samples were analyzed using liquid chromatography–electrospray ionization–mass spectrometry on a high-resolution time-of-flight instrument. Tryptic peptides were identified by tandem mass spectrometry. Proteins differing in concentration between the MCs and NCs were identified. Setting A tertiary dementia referral center and a proteomic biomarker discovery laboratory. Participants Fourteen FAD MCs (mean age, 34.2 years; 10 are asymptomatic, 12 have presenilin-1 [PSEN1 ] gene mutations, and 2 have amyloid precursor protein [APP ] gene mutations) and 5 related NCs (mean age, 37.6 years). Results Fifty-six proteins were identified, represented by multiple tryptic peptides showing significant differences between MCs and NCs (46 upregulated and 10 downregulated); 40 of these proteins differed when the analysis was restricted to asymptomatic individuals. Fourteen proteins have been reported in prior proteomic studies in late-onset AD, including amyloid precursor protein, transferrin, α 1 β-glycoprotein, complement components, afamin precursor, spondin 1, plasminogen, hemopexin, and neuronal pentraxin receptor. Many other proteins were unique to our study, including calsyntenin 3, AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) 4 glutamate receptor, CD99 antigen, di- N-acetyl-chitobiase, and secreted phosphoprotein 1. Conclusions We found much overlap in CSF protein changes between individuals with presymptomatic and symptomatic FAD and those with late-onset AD. Our results are consistent with inflammation and synaptic loss early in FAD and suggest new presymptomatic biomarkers of potential usefulness in drug development.

CCI-779 (CCI) is a novel mTOR kinase inhibitor being developed for treatment of various cancers. In this randomized, double-blind, multicenter trial, 25, 75, or 250 mg IV CCI was given once weekly to 110 patients with renal cancer and was shown to be clinically active (Atkins et al. Proc ASCO 22:201,2003). Herein, the population pharmacokinetics (PPK) of CCI and sirolimus (SIR) were evaluated for dynamic relationship to safety, activity, and pharmacogenomic responses. With correlation analysis, we explored the relationship between gene expression changes in peripheral blood mononuclear cells (PBMC) and cumulative (∑)AUC and AUCsum (CCI+SIR) at 8 and 16 weeks of treatment. Exposures were related to adverse events (AE) and survival. Final PPK models of CCI and SIR included 235 and 305 observations, respectively, from 50 patients. CCI clearance = 1.39*(1+DNUM*0.103)*DOSE0.551 *BSA1.28 in which DNUM = 0/1 for single/multiple dose and BSA = body surface area. Age, sex, race, or renal risk factor did not affect disposition. AE severity correlated to AUCsum for thrombocytopenia, pruritis, and hyperlipemia (p < 0.05). Average AUC or AUCsum were not related to survival differences. At 16 weeks, individual ∑AUCsum was significantly correlated with change from baseline gene expression levels for 19 transcripts. Improved survival in the absence of an exposure relationship suggests that test doses are at or near a plateau of maximum response. A transcript subset from PBMCs significantly correlated with ∑AUCsum and may qualify as biomarkers of CCI exposure. Clinical Pharmacology & Therapeutics (2004) 75, P41–P41; doi: 10.1016/j.clpt.2003.11.155