Perspectives on statistical strategies for the regulatory biomarker qualification process.

Qualification of a biomarker for use in a medical product development program requires a statistical strategy that aligns available evidence with the proposed context of use (COU), identifies any data gaps to be filled and plans any additional research required to support the qualification. Accumulating, interpreting and analyzing available data is outlined, step-by-step, illustrated by a qualified enrichment biomarker example and a safety biomarker in the process of qualification. The detailed steps aid requestors seeking qualification of biomarkers, allowing them to organize the available evidence and identify potential gaps. This provides a statistical perspective for assessing evidence that parallels clinical considerations and is intended to guide the overall evaluation of evidentiary criteria to support a specific biomarker COU.

Inclusion of genetic variants in an ensemble of gradient boosting decision trees does not improve the prediction of citalopram treatment response.

Identifying in advance who is unlikely to respond to a specific antidepressant treatment is crucial to precision medicine efforts. The current work leverages genome-wide genetic variation and machine learning to predict response to the antidepressant citalopram using data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 1257 with both valid genomic and outcome data). A confirmatory approach selected 11 SNPs previously reported to predict response to escitalopram in a sample different from the current study. A novel exploratory approach selected SNPs from across the genome using nested cross-validation with elastic net logistic regression with a predominantly lasso penalty (alpha = 0.99). SNPs from each approach were combined with baseline clinical predictors and treatment response outcomes were predicted using a stacked ensemble of gradient boosting decision trees. Using pre-treatment clinical and symptom predictors only, out-of-fold prediction of a novel treatment response definition based on STAR*D treatment guidelines was acceptable, AUC = .659, 95% CI [0.629, 0.689]. The inclusion of SNPs using confirmatory or exploratory selection methods did not improve the out-of-fold prediction of treatment response (AUCs were .662, 95% CI [0.632, 0.692] and .655, 95% CI [0.625, 0.685], respectively). A similar pattern of results were observed for the secondary outcomes of the presence or absence of distressing side effects regardless of treatment response and achieving remission or satisfactory partial response, assuming medication tolerance. In the current study, incorporating SNP variation into prognostic models did not enhance the prediction of citalopram response in the STAR*D sample.

Predictive Value of a Thyroid-Absorbed Dose with a Shorter Effective Half-Life on Efficacy in Graves Disease Patients Receiving Iodine-131 Therapy.

BACKGROUND Although radioiodine therapy (RIT) efficacy is thoroughly validated for Graves disease (GD), there is a lack of research on the predictive factors of RIT, especially the optimal thyroid-absorbed dose (TD) with a shorter effective half-life (Teff ≤5 days). The goal of this study was to explore the predictive value of TD in GD patients receiving RIT with a shorter Teff. MATERIAL AND METHODS We studied 208 GD patients receiving RIT with a shorter Teff. Plotting the receiver-operating characteristic (ROC) curve verified the accuracy of TD for predicting RIT efficacy in GD patients. In addition, we conducted univariate and multivariate analyses to investigate the influence of 14 factors, including thyroid weight, TD, 24-h radioiodine uptake rate (RAIU), the highest RAIU, thyrotrophin receptor antibody level, thyroglobulin antibody level, thyroid peroxidase antibody level, and others, on curative effects of RIT. RESULTS Of the 208 study participants, complete remission and the total effectiveness rates were 68.3% and 92.3%, respectively. The threshold value of TD to predict RIT efficacy was 70.2 Gy, based on ROC analysis. Univariate analysis showed that 24-h RAIU, Teff, total iodine dose, iodine dose per gram of thyroid tissue, TD, and thyrotropin receptor antibody level were significantly associated with RIT efficacy. Multivariate analysis indicated that 24-h RAIU, total iodine dose, iodine dose per gram of thyroid tissue, and TD were significant independent predictors of RIT efficacy. CONCLUSIONS Predicting RIT efficacy from TD with a shorter Teff was feasible in GD patients, and TD above 70.2 Gy had an especially high predictive accuracy.

Clinical implementation of preemptive pharmacogenomics in psychiatry: Τhe "PREPARE" study.

PREemptive Pharmacogenomic testing for Preventing Adverse drug REactions (PREPARE) is the first prospective, pre-emptive pharmacogenomic study conducted in Europe, within the frame of the Horizon 2020 program. It aims to determine whether implementing pre-emptive pharmacogenomics (PGx) testing of clinically relevant biomarkers, so as the dose and drug selection to be guided, will result in an overall reduction of both the occurrence and the severity of drug-genotype-associated adverse drug reactions (ADRs). To achieve that, two groups of patients will be recruited; one that will receive treatment according to standard clinical practice and one other that will receive pharmacogenomic-guided treatment. The Laboratory of Pharmacogenomics and Individualized Treatment of the University of Patras, which coordinates and represents Greece in this study, in collaboration with the Department of Psychiatry of the General University Hospital of Patras, the Department of Psychiatry of the Hospital "Attikon" and the Departments of Psychiatry of the Psychiatric Hospital of Athens "Dafni" is going to recruit 1500 psychiatric patients that are going to receive antidepressant or antipsychotic treatment. Our scientific hypothesis is that patients who receive pharmacogenomic guided drug and dose selection will experience 30% less ADRs than patients following standard care. Eligible drugs for inclusion in the PREPARE study, are those for which the clinical decision regarding drug and dose choice can be guided according to the Dutch Pharmacogenomics Working Group Guidelines (DPWG). Overall, 7 antidepressants (citalopram, escitalopram, sertraline, paroxetine, venlafaxine, clomipramine, amitriptyline) and 3 antipsychotics (haloperidol, zuclopenthixol, aripiprazole) related to 17 genetic variations in 2 genes (CYP2D6, CYP2C19) will be examined. Occurrence, severity and causality of adverse drug events (ADEs) will be assessed during monitoring, at month 1 and 3 after starting the index-drug, and at the end of each arm, by using the Common Toxicity Criteria for Adverse Events Scale (CTCAE) and the Liverpool Causality Assessment Tool (LCAT), respectively. The results of our study are expected to significantly contribute to the improvement of psychiatric patients' quality of life, by helping to provide the right drug, to the right dose in terms of efficacy, safety and cost-effectiveness.

Assessment of antimalarial drug resistant markers in asymptomatic Plasmodium falciparum infections after 4 years of indoor residual spraying in Northern Ghana.

BACKGROUND: Drug resistance remains a concern for malaria control and elimination. The effect of interventions on its prevalence needs to be monitored to pre-empt further selection. We assessed the prevalence of Plasmodium falciparum gene mutations associated with resistance to the antimalarial drugs: sulfadoxine-pyrimethamine (SP), chloroquine (CQ) and artemisinin combination therapy (ACTs) after the scale-up of a vector control activity that reduced transmission. METHODS: A total of 400 P. falciparum isolates from children under five years were genotyped for seventeen single nucleotide polymorphisms (SNPs) in pfcrt, pfmdr1, pfdhfr, pfdhps and pfk13 genes using polymerase chain reaction (PCR) and high resolution melting (HRM) analysis. These included 80 isolates, each randomly selected from cross-sectional surveys of asymptomatic infections across 2010 (baseline), 2011, 2012, 2013 (midline: post-IRS) and 2014 (endline: post-IRS) during the peak transmission season, when IRS intervention was rolled out in Bunkpurugu Yunyoo (BY) District, Ghana. The proportions of isolates with drug resistant alleles were assessed over this period. RESULTS: There were significant decreases in the prevalence of pfdhfr- I51R59N108 haplotype from 2010 to 2014, while the decline in pfdhfr/pfdhps- I51R59N108G437 during the same period was not significant. The prevalence of lumefantrine (LM), mefloquine (MQ) and amodiaquine (AQ) resistance-associated haplotypes pfmdr1-N86F184D1246 and pfmdr1-Y86Y184Y1246 showed decreasing trends (z = -2.86, P = 0.004 and z = -2.71, P = 0.007, respectively). Each of pfcrt-T76 and pfmdr1-Y86 mutant alleles also showed a declining trend in the asymptomatic reservoir, after the IRS rollout in 2014 (z = -2.87, P = 0.004 and z = -2.65, P = 0.008, respectively). Similarly, Pyrimethamine resistance mediating polymorphisms pfdhfr-N108, pfdhfr-I51 and pfdhfr-R59 also declined (z = -2.03, P = 0.042, z = -3.54, P<0.001 and z = -4.63, P<0.001, respectively), but not the sulphadoxine resistance mediating pfdhps-G437 and pfdhps-F436 (z = -0.36, P = 0.715 and z = 0.41, P = 0.684, respectively). No mutant pfk13-Y580 were detected during the study period. CONCLUSION: The study demonstrated declining trends in the prevalence of drug resistant mutations in asymptomatic P. falciparum infections following transmission reduction after an enhanced IRS intervention in Northern Ghana.

Network Pharmacology and Bioactive Equivalence Assessment Integrated Strategy Driven Q-markers Discovery for Da-Cheng-Qi Decoction to Attenuate Intestinal Obstruction.

BACKGROUND: Intestinal obstruction (IO) is a kind of acute abdomen with high morbidity and mortality. Patients suffer from poor quality of life and tremendous financial pressure. Da-Cheng-Qi decoction (DCQD), a classical purgation prescription, has clinically been proven to be an effective treatment for IO. PURPOSE: Network pharmacology integrated with bioactive equivalence assessment was used to discover the quality marker (Q-marker) of DCQD against IO. METHODS: As there is hardly any targets recorded in database, thus the collection of IO targets was conducted by searching those of alternative diseases which have similar pathological symptoms with IO. In order to improve the reliability of the obtained targets, IO metabolomics data was introduced. Active compounds combination (ACC) was focused as potential Q-markers via component-target network analysis and function query from the identified components corresponding to the common targets. Bioequivalence between ACC and DCQD was assessed from the aspects of intestine motility (somatostatin secretion), inflammation (IL-6 secretion) and injury (wound healing assay) in vitro and was further validated in ileus rat model. PPI network analysis of core targets followed by gene pedigree classification and experimental validation confirmed the potential intervention pathway. RESULTS: A combination of 11 ingredients, including emodin, physcion, aloe-emodin, rhein, chrysophanol, gallic acid, magnolol, honokiol, naringenin, tangeretin, and nobiletin was finally confirmed bioequivalence with DQCD to some extent and could serve as Q-markers for DCQD to attenuate IO. PI3K/AKT was verified as a possible affected pathway that DCQD exerted the effectiveness against IO. CONCLUSION: For the disease with few recorded targets, searching those of alternative diseases which have similar pathological symptoms could be a feasible and effective approach. The proposed network pharmacology integrated bioactive equivalence evaluation paradigm is efficient to discover Q-marker of herbal formulae.

Comparing outcomes and costs among warfarin-sensitive patients versus warfarin-insensitive patients using The Right Drug, Right Dose, Right Time: Using genomic data to individualize treatment (RIGHT) 10K warfarin cohort.

Oral anticoagulant (OAC) therapy has been the main treatment approach for stroke prevention for decades. Warfarin is the most widely prescribed OAC in the United States, but is difficult to manage due to variability in dose requirements across individuals. Pharmacogenomics may mitigate risk concerns related to warfarin use by fostering the opportunity to facilitate individualized medicine approaches to warfarin treatment (e.g., genome-guided dosing). While various economic evaluations exist examining the cost-effectiveness of pharmacogenomics testing for warfarin, few observational studies exist to support these studies, with even fewer using genotype as the main exposure of interest. We examined a cohort of individuals initiating warfarin therapy between 2004 and 2017 and examined bleeding and cost outcomes for the year following initiation using Mayo Clinic's billing and administrative data, as well the Mayo Clinic Rochester Cost Data Warehouse. Analyses included descriptive summaries, comparison of characteristics across exposure groups, reporting of crude outcomes, and multivariate analyses. We included N = 1,143 patients for analyses. Just over a third of our study population (34.9%) carried a warfarin-sensitive phenotype. Sensitive individuals differed in their baseline characteristics by being of older age and having a higher number of comorbid conditions; myocardial infarction, diabetes, and cancer in particular. The occurrence of bleeding events was not significantly different across exposure groups. No significant differences across exposure groups existed in either the likelihood of incurring all-cause healthcare costs or in the magnitude of those costs. Warfarin-sensitive individuals were no more likely to utilize cardiovascular-related healthcare services; however, they had lower total and inpatient cardiovascular-related costs compared to warfarin-insensitive patients. No significant differences existed in any other categories of costs. We found limited evidence that warfarin-sensitive individuals have different healthcare spending than warfarin-insensitive individuals. Additional real-world studies are needed to support the traditional economic evaluations currently existing in the literature.

Ki-67 assessment in early breast cancer: SAKK28/12 validation study on the IBCSG VIII and IBCSG IX cohort.

The assessment of Ki-67 in early-stage breast cancer has become an important diagnostic tool in planning adjuvant therapy, particularly for the administration of additional chemotherapy to hormone-responsive patients. An accurate determination of the Ki-67 index is of the utmost importance; however, the reproducibility is currently unsatisfactory. In this study, we addressed the predictive/prognostic value of Ki-67 index assessed by using the most reproducible methods, which were identified in the pilot phase. Paraffin blocks obtained from patients with moderately differentiated, estrogen receptor (ER)-positive early-stage breast cancer in Switzerland, who were originally randomized to the treatment arms with and without chemotherapy in the IBCSG VIII-IX trials, were retrieved. Of these 344 randomized patients, we identified 158 patients (82 treated with and 76 treated without chemotherapy) for whom sufficient tumour tissue was available. The presence of Ki-67 was assessed visually by counting 2000 cells at the periphery (A) and estimating the number of positive cells in five different peripheral regions (C), which was determined to be the most reproducible method identified the pilot phase. The prognostic and predictive value was assessed by calculating the breast cancer-free interval (BCFI) and overall survival (OS) rate. Ki-67 was considered a numerical and categorical variable when different cut-off values were used (10%, 14%, 20% and 30%). An mRNA-based subtyping by using the MammaTyper kit with the application of a 20% Ki-67 immunohistochemistry (IHC) cut-off equivalent was also performed. 158 of 344 randomized patients could be included in the Ki-67 analysis. The mean Ki-67 values obtained by using the two methods differed (A: 21.32% and C: 16.07%). Ki-67 assessed by using method A with a cut-off of 10% was a predictive marker for OS, as the hazard ratio (>10% vs. <=10%) in patients with chemotherapy was 0.48 with a 95% confidence interval of [0.19-1.19]. Further, the HR of patients treated without chemotherapy was 3.72 with a 95% confidence interval of [1.16-11.96] (pinteraction=0.007). Higher Ki-67 index was not associated with outcome and using the 10% Ki-67 cut-off there was an opposite association for patients with and without chemotherapy. Ki-67 assessments with IHC significantly correlated with MammaTyper results (p=0.002). The exact counting method (A) performed via a light-microscope revealed the predictive value of Ki-67 assessment with a 10% cut-off value. Further analyses employing image analyses and/or mRNA-based-assessments in larger populations are warranted.

Lymphoma and Leukemia Cell Vulnerabilities and Resistance Identified by Compound Library Screens.

Response to anticancer agents is often restricted to subsets of patients. The recognition of factors underlying this heterogeneity and the identification of biomarkers associated with response to drugs would greatly improve the efficacy of drug treatment. Platforms that can comprehensively map drug response in high-throughput ex vivo provide a unique tool to identify associated biomarkers and provide hypotheses for mechanisms underlying variable response. Such screens can be performed on cell lines and short-term cultures of primary cells to take advantage of the respective models' strength, which include, e.g., the ability to silence genes in cell lines and the "indefinite" supply of primary cells where clonal selection can be avoided. Cohorts of such samples represent the natural diversity of cancers, including rarer mutations and combinatorial patterns of mutations.We here summarize a simple and scalable method for the measurement of viability after drug exposure based on ATP measurements as a surrogate for viability, which we use to measure and understand drug response in cell lines and primary cells.

Establishment of a novel safety assessment method for vaccine adjuvant development.

Vaccines effectively prevent infectious diseases. Many types of vaccines against various pathogens that threaten humans are currently in widespread use. Recently, adjuvant adaptation has been attempted to activate innate immunity to enhance the effectiveness of vaccines. The effectiveness of adjuvants for vaccinations has been demonstrated in many animal models and clinical trials. Although a highly potent adjuvant tends to have high effectiveness, it also has the potential to increase the risk of side effects such as pain, edema, and fever. Indeed, highly effective adjuvants, such as poly(I:C), have not been clinically applied due to their high risks of toxicity in humans. Therefore, the task in the field of adjuvant development is to clinically apply highly effective and non- or low-toxic adjuvant-containing vaccines. To resolve this issue, it is essential to ensure a low risk of side effects and the high efficacy of an adjuvant in the early developmental phases. This review summarizes the theory and history of the current safety assessment methods for adjuvants, using the inactivated influenza vaccine as a model. Our novel method was developed as a system to judge the safety of a candidate compound using biomarkers identified by genomic technology and statistical tools. A systematic safety assessment tool for adjuvants would be of great use for predicting toxicity during novel adjuvant development, screening, and quality control.

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study.

Modernizing Human Cancer Risk Assessment of Therapeutics.

Cancer risk assessment of therapeutics is plagued by poor translatability of rodent models of carcinogenesis. In order to overcome this fundamental limitation, new approaches are needed that enable us to evaluate cancer risk directly in humans and human-based cellular models. Our enhanced understanding of the mechanisms of carcinogenesis and the influence of human genome sequence variation on cancer risk motivates us to re-evaluate how we assess the carcinogenic risk of therapeutics. This review will highlight new opportunities for applying this knowledge to the development of a battery of human-based in vitro models and biomarkers for assessing cancer risk of novel therapeutics.

[Can positron emission tomography assessment of response to treatment help to individualize use of erlotinib in non-small cell lung cancer?] / L'imagerie par émission de positons à la 3'-désoxy-3'-[18F]-fluorothymidine ([18F]FLT) peut-elle avoir un rôle théranostique lors du traitement par erlotinib dans le cancer bronchique non à petites cellules ?

Erlotinib can be prescribed in the treatment of locally advanced or metastatic non-small lung cancer cell (NSCLC) after failure of at least one prior chemotherapy regimen on the basis of the BR-21 study. Several publications have recently questioned these results. The metabolic imaging of solid tumours by positron emission tomography is a research field that could help customize the treatment of NSCLC and so complement the treatment approaches allowed by genetic analyses. This strategy is part of an innovative "early metabolic look" approach. The primary objective of this study is to determine if metabolic progression observed between the 7th and 14th day after initiation of treatment with erlotinib by 3'-Deoxy-3'-[18F]-Fluorothymidine PET in patients with EGFR naive NSCLC is predictive for morphological progression after 6 to 8 weeks of treatment. A health economic analysis will be conducted. This study is particularly innovative because it begins the exploration of the era of metabolic evaluation of therapeutic response in NSCLC.

RNA-Seq-based toxicogenomic assessment of fresh frozen and formalin-fixed tissues yields similar mechanistic insights.

Formalin-fixed, paraffin-embedded (FFPE) pathology specimens represent a potentially vast resource for transcriptomic-based biomarker discovery. We present here a comparison of results from a whole transcriptome RNA-Seq analysis of RNA extracted from fresh frozen and FFPE livers. The samples were derived from rats exposed to aflatoxin B1 (AFB1 ) and a corresponding set of control animals. Principal components analysis indicated that samples were separated in the two groups representing presence or absence of chemical exposure, both in fresh frozen and FFPE sample types. Sixty-five percent of the differentially expressed transcripts (AFB1 vs. controls) in fresh frozen samples were also differentially expressed in FFPE samples (overlap significance: P < 0.0001). Genomic signature and gene set analysis of AFB1 differentially expressed transcript lists indicated highly similar results between fresh frozen and FFPE at the level of chemogenomic signatures (i.e., single chemical/dose/duration elicited transcriptomic signatures), mechanistic and pathology signatures, biological processes, canonical pathways and transcription factor networks. Overall, our results suggest that similar hypotheses about the biological mechanism of toxicity would be formulated from fresh frozen and FFPE samples. These results indicate that phenotypically anchored archival specimens represent a potentially informative resource for signature-based biomarker discovery and mechanistic characterization of toxicity.

Considerations for assessment of reproductive and developmental toxicity of oligonucleotide-based therapeutics.

This white paper summarizes the current consensus of the Reproductive Subcommittee of the Oligonucleotide Safety Working Group on strategies to assess potential reproductive and/or developmental toxicities of therapeutic oligonucleotides (ONs). The unique product characteristics of ONs require considerations when planning developmental and reproductive toxicology studies, including (a) chemical characteristics, (b) assessment of intended and unintended mechanism of action, and (c) the optimal exposure, including dosing regimen. Because experience across the various classes of ONs as defined by their chemical backbone is relatively limited, best practices cannot be defined. Rather, points to consider are provided to help in the design of science-based reproductive safety evaluation programs based upon product attributes.

An industry perspective on tiered approach to the investigation of metabolites in drug development.

BACKGROUND: A tiered approach to drug metabolite measurement and identification is often used industry wide to fulfill regulatory requirements specified in recent US FDA and European Medicines Agency guidance. Although this strategy is structured in its intent it can be customized to address unique challenges which may arise during early and late drug development activities. These unconventional methods can be applied at any stage to facilitate metabolite characterization. RESULTS: Two case studies are described NVS 1 and 2. NVS 1: plasma concentrations, measured using a radiolabeled MS-response factor exploratory method, were comparable to those from a validated bioanalytical method. The NVS 2 example showed how in vitro analysis helped to characterize an unexpectedly abundant circulating plasma metabolite M3. CONCLUSION: A tiered approach incorporating many aspects of conventional and flexible analytical methodologies can be pulled together to address regulatory questions surrounding drug metabolite characterization.

T-cell dependent immunogenicity of protein therapeutics: Preclinical assessment and mitigation.

Protein therapeutics hold a prominent and rapidly expanding place among medicinal products. Purified blood products, recombinant cytokines, growth factors, enzyme replacement factors, monoclonal antibodies, fusion proteins, and chimeric fusion proteins are all examples of therapeutic proteins that have been developed in the past few decades and approved for use in the treatment of human disease. Despite early belief that the fully human nature of these proteins would represent a significant advantage, adverse effects associated with immune responses to some biologic therapies have become a topic of some concern. As a result, drug developers are devising strategies to assess immune responses to protein therapeutics during both the preclinical and the clinical phases of development. While there are many factors that contribute to protein immunogenicity, T cell- (thymus-) dependent (Td) responses appear to play a critical role in the development of antibody responses to biologic therapeutics. A range of methodologies to predict and measure Td immune responses to protein drugs has been developed. This review will focus on the Td contribution to immunogenicity, summarizing current approaches for the prediction and measurement of T cell-dependent immune responses to protein biologics, discussing the advantages and limitations of these technologies, and suggesting a practical approach for assessing and mitigating Td immunogenicity.

Early assessment of the efficacy of temozolomide chemotherapy in experimental glioblastoma using [18F]FLT-PET imaging.

UNLABELLED: Addition of temozolomide (TMZ) to radiation therapy is the standard treatment for patients with glioblastoma (GBM). However, there is uncertainty regarding the effectiveness of TMZ. Considering the rapid evolution of the disease, methods to assess TMZ efficacy early during treatment would be of great benefit. Our aim was to monitor early effects of TMZ in a mouse model of GBM using positron emission tomography (PET) with 3'-deoxy-3'-[(18)F]fluorothymidine ([(18)F]FLT). METHODS: Human glioma cells sensitive to TMZ (Gli36dEGFR-1) were treated with sub-lethal doses of TMZ to obtain cells with lower sensitivity to TMZ (Gli36dEGFR-2), as measured by growth and clonogenic assays. Gli36dEGFR-1 and Gli36dEGFR-2 cells were subcutaneously (s.c.) or intracranially (i.c.) xenografted into nude mice. Mice were treated for 7 days with daily injection of 25 or 50 mg/kg TMZ. Treatment efficacy was measured using [(18)F]FLT-PET before treatment and after 2 days. Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) were used to determine tumor volumes before treatment and after 7 days. RESULTS: A significant difference was observed between TMZ and DMSO treated tumors in terms of variations of [(18)F]FLT T/B ratio as soon as day 2 in the i.c. as well as in the s.c. mouse model. Variations of [(18)F]FLT T/B uptake ratio between days 0 and 2 correlated with variations of tumor size between days 0 and 7 (s.c. model: n(tumor) = 17 in n(mice) = 11, P<0.01; i.c. model: n(tumor/mice) = 9, P<0.01). CONCLUSIONS: Our results indicate that [(18)F]FLT-PET may be useful for an early evaluation of the response of GBM to TMZ chemotherapy in patients with glioma.

Metabolomics and its potential in drug development.

Metabolomics is the global and unbiased survey of the complement of small molecules (say, <1 kDa) in a biofluid, tissue, organ or organism and measures the end-products of the cellular metabolism of both endogenous and exogenous substrates. Many drug candidates fail during Phase II and III clinical trials at an enormous cost to the pharmaceutical industry in terms of both time lost and of financial resources. The constantly evolving model of drug development now dictates that biomarkers should be employed in preclinical development for the early detection of likely-to-fail candidates. Biomarkers may also be useful in the preselection of patients and through the subclassification of diseases in clinical drug development. Here we show with examples how metabolomics can assist in the preclinical development phases of discovery, pharmacology, toxicology, and ADME. Although not yet established as a clinical trial patient prescreening procedure, metabolomics shows considerable promise in this regard. We can be certain that metabolomics will join genomics and transcriptomics in lubricating the wheels of clinical drug development in the near future.