Molecular modeling of peptide-MHC class I complexes : applications to peptide based immunotherapy

Summary The specific CD8+ T cell immune response against tumors relies on the recognition by the T cell receptor (TCR) on cytotoxic T lymphocytes (CTL) of antigenic peptides bound to the class I major histocompatibility complex (MHC) molecule. Such tumor associated antigenic peptides are the focus of tumor immunotherapy with peptide vaccines. The strategy for obtaining an improved immune response often involves the design of modified tumor associated antigenic peptides. Such modifications aim at creating higher affinity and/or degradation resistant peptides and require precise structures of the peptide-MHC class I complex. In addition, the modified peptide must be cross-recognized by CTLs specific for the parental peptide, i.e. preserve the structure of the epitope. Detailed structural information on the modified peptide in complex with MHC is necessary for such predictions. In this thesis, the main focus is the development of theoretical in silico methods for prediction of both structure and cross-reactivity of peptide-MHC class I complexes. Applications of these methods in the context of immunotherapy are also presented. First, a theoretical method for structure prediction of peptide-MHC class I complexes is developed and validated. The approach is based on a molecular dynamics protocol to sample the conformational space of the peptide in its MHC environment. The sampled conformers are evaluated using conformational free energy calculations. The method, which is evaluated for its ability to reproduce 41 X-ray crystallographic structures of different peptide-MHC class I complexes, shows an overall prediction success of 83%. Importantly, in the clinically highly relevant subset of peptide-HLAA*0201 complexes, the prediction success is 100%. Based on these structure predictions, a theoretical approach for prediction of cross-reactivity is developed and validated. This method involves the generation of quantitative structure-activity relationships using three-dimensional molecular descriptors and a genetic neural network. The generated relationships are highly predictive as proved by high cross-validated correlation coefficients (0.78-0.79). Together, the here developed theoretical methods open the door for efficient rational design of improved peptides to be used in immunotherapy. Résumé La réponse immunitaire spécifique contre des tumeurs dépend de la reconnaissance par les récepteurs des cellules T CD8+ de peptides antigéniques présentés par les complexes majeurs d'histocompatibilité (CMH) de classe I. Ces peptides sont utilisés comme cible dans l'immunothérapie par vaccins peptidiques. Afin d'augmenter la réponse immunitaire, les peptides sont modifiés de façon à améliorer l'affinité et/ou la résistance à la dégradation. Ceci nécessite de connaître la structure tridimensionnelle des complexes peptide-CMH. De plus, les peptides modifiés doivent être reconnus par des cellules T spécifiques du peptide natif. La structure de l'épitope doit donc être préservée et des structures détaillées des complexes peptide-CMH sont nécessaires. Dans cette thèse, le thème central est le développement des méthodes computationnelles de prédiction des structures des complexes peptide-CMH classe I et de la reconnaissance croisée. Des applications de ces méthodes de prédiction à l'immunothérapie sont également présentées. Premièrement, une méthode théorique de prédiction des structures des complexes peptide-CMH classe I est développée et validée. Cette méthode est basée sur un échantillonnage de l'espace conformationnel du peptide dans le contexte du récepteur CMH classe I par dynamique moléculaire. Les conformations sont évaluées par leurs énergies libres conformationnelles. La méthode est validée par sa capacité à reproduire 41 structures des complexes peptide-CMH classe I obtenues par cristallographie aux rayons X. Le succès prédictif général est de 83%. Pour le sous-groupe HLA-A*0201 de complexes de grande importance pour l'immunothérapie, ce succès est de 100%. Deuxièmement, à partir de ces structures prédites in silico, une méthode théorique de prédiction de la reconnaissance croisée est développée et validée. Celle-ci consiste à générer des relations structure-activité quantitatives en utilisant des descripteurs moléculaires tridimensionnels et un réseau de neurones couplé à un algorithme génétique. Les relations générées montrent une capacité de prédiction remarquable avec des valeurs de coefficients de corrélation de validation croisée élevées (0.78-0.79). Les méthodes théoriques développées dans le cadre de cette thèse ouvrent la voie du design de vaccins peptidiques améliorés.

Risk-benefit balance assessment of SSRI antidepressant use during pregnancy and lactation based on best available evidence.

INTRODUCTION: Psychiatric disorders are among the leading causes of disability in Western societies. Selective serotonin reuptake inhibitors (SSRIs) are the most frequently prescribed antidepressant drugs during pregnancy and the postpartum period. Over the last decade, conflicting findings regarding the safety of SSRI drugs during pregnancy and lactation have questioned whether such treatments should be used during this period. AREAS COVERED: We discuss the main criteria that should be considered in the risk/benefit assessment of SSRI treatment in pregnant and/or breastfeeding patients (i.e., risks associated with SSRI use and with untreated depression as well as therapeutic benefits of SSRI and some alternative treatment strategies). For each criterion, available evidence has been synthesized and stratified by methodological quality as well as discussed for clinical impact. EXPERT OPINION: Currently, it is impossible for most of the evaluated outcomes to distinguish between the effects related to the mother's underlying disease and those inherent to SSRI treatment. In women suffering from major depression and responding to a pharmacological treatment, introduction or continuation of an SSRI should be encouraged in order to prevent maternal complications and to preserve maternal-infant bonding. The choice of the right drug depends above all on individual patient characteristics such as prior treatment response, diagnoses and comorbid conditions.

Community-Based Corrections Substance Abuse Treatment for the Higher Risk Offender, Iowa Department of Corrections Performance Audit Report, September 2007

The performance audit conducted by the Department of Management concerned the licensed substance abuse treatment programs in Department of Corrections’ institutions. This report uses the same methodology, modified for community-based corrections populations, to examine the delivery of substance abuse treatment for higher risk offenders under field supervision, and all offenders who were assigned to community corrections residential facilities.

Gestational diabetes mellitus and the risk of metabolic syndrome: a population-based study in Lausanne, Switzerland.

AIMS: To investigate the relationships between gestational diabetes mellitus (GDM) and the metabolic syndrome (MS), as it was suggested that insulin resistance was the hallmark of both conditions. To analyse post-partum screening in order to identify risk factors for the subsequent development of type 2 diabetes mellitus (DM). METHODS: A retrospective analysis of all singleton pregnancies diagnosed with GDM at the Lausanne University Hospital for 3 consecutive years. Pre-pregnancy obesity, hypertension and dyslipidaemia were recorded as constituents of the MS. RESULTS: For 5788 deliveries, 159 women (2.7%) with GDM were identified. Constituents of the MS were present before GDM pregnancy in 26% (n = 37/144): 84% (n = 31/37) were obese, 38% (n = 14/37) had hypertension and 22% (n = 8/37) had dyslipidaemia. Gestational hypertension was associated with obesity (OR = 3.2, P = 0.02) and dyslipidaemia (OR = 5.4, P=0.002). Seventy-four women (47%) returned for post-partum OGTT, which was abnormal in 20 women (27%): 11% (n = 8) had type 2 diabetes and 16% (n = 12) had impaired glucose tolerance. Independent predictors of abnormal glucose tolerance in the post-partum were: having > 2 abnormal values on the diagnostic OGTT during pregnancy and presenting MS constituents (OR = 5.2, CI 1.8-23.2 and OR = 5.3, CI 1.3-22.2). CONCLUSIONS: In one fourth of GDM pregnancies, metabolic abnormalities precede the appearance of glucose intolerance. These women have a high risk of developing the MS and type 2 diabetes in later years. Where GDM screening is not universal, practitioners should be aware of those metabolic risks in every pregnant woman presenting with obesity, hypertension or dyslipidaemia, in order to achieve better diagnosis and especially better post-partum follow-up and treatment.

Measurements of glial metabolic fluxes with C-11-acetate using positron emission and an adapted NMR-based metabolic modeling approach

Objectives: Acetate brain metabolism has the particularity to occur specifically in glial cells. Labeling studies, using acetate labeled either with 13C (NMR) or 11C (PET), are governed by the same biochemical reactions and thus follow the same mathematical principles. In this study, the objective was to adapt an NMR acetate brain metabolism model to analyse [1-11C]acetate infusion in rats. Methods: Brain acetate infusion experiments were modeled using a two-compartment model approach used in NMR.1-3 The [1-11C]acetate labeling study was done using a beta scintillator.4 The measured radioactive signal represents the time evolution of the sum of all labeled metabolites in the brain. Using a coincidence counter in parallel, an arterial input curve was measured. The 11C at position C-1 of acetate is metabolized in the first turn of the TCA cycle to the position 5 of glutamate (Figure 1A). Through the neurotransmission process, it is further transported to the position 5 of glutamine and the position 5 of neuronal glutamate. After the second turn of the TCA cycle, tracer from [1-11C]acetate (and also a part from glial [5-11C]glutamate) is transferred to glial [1-11C]glutamate and further to [1-11C]glutamine and neuronal glutamate through the neurotransmission cycle. Brain poster session: oxidative mechanisms S460 Journal of Cerebral Blood Flow & Metabolism (2009) 29, S455-S466 Results: The standard acetate two-pool PET model describes the system by a plasma pool and a tissue pool linked by rate constants. Experimental data are not fully described with only one tissue compartment (Figure 1B). The modified NMR model was fitted successfully to tissue time-activity curves from 6 single animals, by varying the glial mitochondrial fluxes and the neurotransmission flux Vnt. A glial composite rate constant Kgtg=Vgtg/[Ace]plasma was extracted. Considering an average acetate concentration in plasma of 1 mmol/g5 and the negligible additional amount injected, we found an average Vgtg = 0.08±0.02 (n = 6), in agreement with previous NMR measurements.1 The tissue time-activity curve is dominated by glial glutamate and later by glutamine (Figure 1B). Labeling of neuronal pools has a low influence, at least for the 20 mins of beta-probe acquisition. Based on the high diffusivity of CO2 across the blood-brain barrier; 11CO2 is not predominant in the total tissue curve, even if the brain CO2 pool is big compared with other metabolites, due to its strong dilution through unlabeled CO2 from neuronal metabolism and diffusion from plasma. Conclusion: The two-compartment model presented here is also able to fit data of positron emission experiments and to extract specific glial metabolic fluxes. 11C-labeled acetate presents an alternative for faster measurements of glial oxidative metabolism compared to NMR, potentially applicable to human PET imaging. However, to quantify the relative value of the TCA cycle flux compared to the transmitochondrial flux, the chemical sensitivity of NMR is required. PET and NMR are thus complementary.

Gestational diabetes mellitus and the risk of metabolic syndrome: a population-based study in Lausanne, Switzerland

RESUME EN FRANÇAIS BUTS. Étudier les relations entre le diabète gestationnel (GDM) et le syndrome métabolique (MS), comme la résistance à l'insuline est une des caractéristiques des 'deux conditions. Analyser le dépistage du diabète dans le post-partum pour identifier les facteurs de risque associés au développement d'un diabète de type 2 ultérieur. MÉTHODES. Étude rétrospective de toutes les grossesses uniques diagnostiquées avec un diabète gestationnel à l'hôpital universitaire de Lausanne, pendant une durée de trois ans. La présence d'une obésité, d'une hypertension ou d'une dyslipidémie avant la grossesse définissent les composants du syndrome métabolique. RÉSULTATS. Sur 5788 grossesses, 159 patientes (2.7%) présentaient un diabète gestationnel. Des composants du syndrome métabolique étaient présents avant la grossesse chez 26% des patientes (n=37/144) : 84% (n=31/37) étaient obèses, 38% (n=14/37) présentaient une hypertension et 22% (n=8/37) une dyslipidémie. Le développement d'une hypertension gravidique était associé à l'obésité (OR=3.2, p=0.02) et à la dyslipidémie (OR=5.4, p=0.002). Septante-quatre patientes (47%) sont revenues pour l'HGPO dans le post-partum. Celle-ci était anormale chez 20 femmes (27%): 11 % (n=8) présentaient un diabète de type 2 et 16% (n=12) avaient une intolérance au glucose. Les facteurs de risque indépendants associés à une anomalie de la tolérance au glucose dans le post-partum étaient d'avoir plus de 2 valeurs anormales au test diagnostique durant la grossesse et présenter des composants du syndrome métabolique (OR=5.2, CI 1.8-23.2 et OR=5.3, CI 1.3-22.2). CONCLUSIONS. Dans un quart des grossesses avec un diabète gestationnel, des anomalies métaboliques précèdent l'apparition de l'intolérance au glucose. Ces patientes présentent un haut risque de développer un syndrome métabolique et un diabète de type 2 ultérieurement. Là où le dépistage du diabète gestationnel n'est pas systématique, les praticiens devraient être avertis de ces risques métaboliques chez les patiente se présentant avec une obésité, une hypertension ou une dyslipidémie, afin de mieux les diagnostiquer et surtout de mieux les suivre et traiter après leur grossesse.

Secondary Cancer Risk for Stage I Seminoma Patients - a Comparison of Adjuvant Treatment Versus Surveillance

Background: Post-surgical management of stage I seminoma includes: surveillance with repeated CT-scans and treatment reserved for those who relapse, or adjuvant treatment with either immediate radiation therapy (RT) or carboplatin. The cancer specific survival is close to 100%. Cure without long-term sequelae of treatment is the aim. Our goal is to estimate the risk of radiation-induced secondary cancers (SC) death from for patients undergoing S, adjuvant RT or adjuvant carboplatin (AC).Materials and Methods: We measured organ doses from CT scans (3 phases each one) of a seminoma patient who was part of the active surveillance strategy and from a man undergoing adjuvant RT 20-Gy and a 30-Gy salvage RT treatment to the para-aortic area using helical Intensity Modulated RT (Tomotherapy®) with accurate delineation of organs at risk and a CTV to PTV expansion of 1 cm. Effective doses to organs in mSv were estimated according to the tissue-weighting factors recommendations of the International Commission on Radiological Protection 103 (Ann ICRP 2007). We estimated SC incidence and mortality for a 10,000 people population based on the excess absolute risk model from the Biological Effects of Ionizing Radiation (BEIR) VII (Health Risk of Exposure to Low Levels of Ionizing Radiation, NCR, The National Academies Press Washington, DC, 2006) assuming a seminoma diagnosis at age 30, a total life expectancy of 80 years.Results: The nominal risk for a fatal secondary cancers was calculated 1.5% for 15 abdominal CT scans, 14.8% for adjuvant RT (20 Gy paraaortic field) and 22.2% for salvage RT (30 Gy). The calculation assumed that the risk of relapse on surveillance and adjuvant AC was 15% and 4% respectively and that all patients were salvaged at relapse with RT. n CT abdomen/Pelvis = secondary cancer % RT Dose and % receiving treatment = secondary cancer % Total secondary cancer risk in % Active surveillance 15 = 1.5% 30 Gy in 15% of pts = 3.3% 4.8 Adjuvant carboplatin 7 = 0.7% 30 Gy in 4% of pts = 0.88% 1.58 Adjuvant radiotherapy 7 = 0.7% 20 Gy in 100% of pts = 14.8% 15.5Conclusions: These data suggest that: 1) Adjuvant radiotherapy is harmful and should not anymore be regarded as a standard option for seminoma stage I. 2) AC seems to be an option to reduce radiation induced cancers. Limitations: the study does not consider secondary cancers due to chemotherapy with AC (unknown). The use of BEIR VII for risk modeling with higher doses of RT needs to be validated.

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

Accuracy of four cardiovascular risk scores in a Swiss population-based cohort

Purpose: To assess the global cardiovascular (CV) risk of an individual, several scores have been developed. However, their accuracy and comparability need to be evaluated in populations others from which they were derived. The aim of this study was to compare the predictive accuracy of 4 CV risk scores using data of a large population-based cohort. Methods: Prospective cohort study including 4980 participants (2698 women, mean age± SD: 52.7±10.8 years) in Lausanne, Switzerland followed for an average of 5.5 years (range 0.2 - 8.5). Two end points were assessed: 1) coronary heart disease (CHD), and 2) CV diseases (CVD). Four risk scores were compared: original and recalibrated Framingham coronary heart disease scores (1998 and 2001); original PROCAM score (2002) and its recalibrated version for Switzerland (IAS-AGLA); Reynolds risk score. Discrimination was assessed using Harrell's C statistics, model fitness using Akaike's information criterion (AIC) and calibration using pseudo Hosmer-Lemeshow test. The sensitivity, specificity and corresponding 95% confidence intervals were assessed for each risk score using the highest risk category ([20+ % at 10 years) as the "positive" test. Results: Recalibrated and original 1998 and original 2001 Framingham scores show better discrimination (>0.720) and model fitness (low AIC) for CHD and CVD. All 4 scores are correctly calibrated (Chi2<20). The recalibrated Framingham 1998 score has the best sensitivities, 37.8% and 40.4%, for CHD and CVD, respectively. All scores present specificities >90%. Framingham 1998, PROCAM and IAS-AGLA scores include the greatest proportion of subjects (>200) in the high risk category whereas recalibrated Framingham 2001 and Reynolds include <=44 subjects. Conclusion: In this cohort, we see variations of accuracy between risk scores, the original Framingham 2001 score demonstrating the best compromise between its accuracy and its limited selection of subjects in the highest risk category. We advocate that national guidelines, based on independently validated data, take into account calibrated CV risk scores for their respective countries.

Efficacy, tolerability and risk factors for virological failure of darunavir-based therapy for treatment-experienced HIV-infected patients: the Swiss HIV Cohort Study.

OBJECTIVES: Darunavir was designed for activity against HIV resistant to other protease inhibitors (PIs). We assessed the efficacy, tolerability and risk factors for virological failure of darunavir for treatment-experienced patients seen in clinical practice. METHODS: We included all patients in the Swiss HIV Cohort Study starting darunavir after recording a viral load above 1000 HIV-1 RNA copies/mL given prior exposure to both PIs and nonnucleoside reverse transcriptase inhibitors. We followed these patients for up to 72 weeks, assessed virological failure using different loss of virological response algorithms and evaluated risk factors for virological failure using a Bayesian method to fit discrete Cox proportional hazard models. RESULTS: Among 130 treatment-experienced patients starting darunavir, the median age was 47 years, the median duration of HIV infection was 16 years, and 82% received mono or dual antiretroviral therapy before starting highly active antiretroviral therapy. During a median patient follow-up period of 45 weeks, 17% of patients stopped taking darunavir after a median exposure of 20 weeks. In patients followed beyond 48 weeks, the rate of virological failure at 48 weeks was at most 20%. Virological failure was more likely where patients had previously failed on both amprenavir and saquinavir and as the number of previously failed PI regimens increased. CONCLUSIONS: As a component of therapy for treatment-experienced patients, darunavir can achieve a similar efficacy and tolerability in clinical practice to that seen in clinical trials. Clinicians should consider whether a patient has failed on both amprenavir and saquinavir and the number of failed PI regimens before prescribing darunavir.

Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Regional deterministic characterization of fracture networks and its application to GIS-based rock fall risk assessment

The study investigates the possibility to incorporate fracture intensity and block geometry as spatially continuous parameters in GIS-based systems. For this purpose, a deterministic method has been implemented to estimate block size (Bloc3D) and joint frequency (COLTOP). In addition to measuring the block size, the Bloc3D Method provides a 3D representation of the shape of individual blocks. These two methods were applied using field measurements (joint set orientation and spacing) performed over a large field area, in the Swiss Alps. This area is characterized by a complex geology, a number of different rock masses and varying degrees of metamorphism. The spatial variability of the parameters was evaluated with regard to lithology and major faults. A model incorporating these measurements and observations into a GIS system to assess the risk associated with rock falls is proposed. The analysis concludes with a discussion on the feasibility of such an application in regularly and irregularly jointed rock masses, with persistent and impersistent discontinuities.

Patterns of Adherence to Raltegravir-Based Regimens and the Risk of Virological Failure Among HIV-Infected Patients: The RALTECAPS Cohort Study.

ABSTRACT:: Adherence patterns and their influence on virologic outcome are well characterized for protease inhibitor (PI)- and non-nucleoside reverse transcriptase inhibitor (NNRTI)-based regimens. We aimed to determine how patterns of adherence to raltegravir influence the risk of virological failure. We conducted a prospective multicenter cohort following 81 HIV-infected antiretroviral-naive or experienced subjects receiving or starting twice-a-day raltegravir-based antiretroviral therapy. Their adherence patterns were monitored using the Medication Events Monitoring System. During follow-up (188 days, ±77), 12 (15%) of 81 subjects experienced virological failure. Longer treatment interruption [adjusted odds ratio per 24-hour increase: 2.4; 95% confidence interval: 1.2 to 6.9; P < 0.02] and average adherence (odds ratio per 5% increase: 0.68; 95% confidence interval: 0.46 to 1.00, P < 0.05) were both independently associated with virological failure controlling for prior duration of viral suppression. Timely interdose intervals and high levels of adherence to raltegravir are both necessary to control HIV replication.