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PubMed2005

De-immunization of therapeutic proteins by T-cell epitope modification.

Many therapeutic proteins in clinical use have been shown to elicit antibody responses which in some cases have been linked to adverse events. Conventional animal models, although convenient, have rarely been predictive of immunogenicity in humans. New methods for predicting the potential immunogenicity of therapeutic proteins are needed. This treatise proposes a new approach which pairs in silico T-cell epitope analysis with in vitro studies. T-cell epitope mapping algorithms such as EpiMatrix can be used to evaluate a candidate therapeutic protein for T-helper epitopes, followed by confirmation of the T-helper epitopes using in vitro methods such as MHC binding assays and T-cell assays. Once these are identified, substitution of key amino acids in the T-cell epitopes may attenuate the immunogenicity of the protein, since modification of the amino acids in anchor position(s) can abrogate binding to human class II MHC molecules and presentation of the peptides, in the context of MHC, to T-helper cells. Following substitution of the key amino acids, immunogenicity of the modified protein can be evaluated in vitro. In parallel, the potential effect of the modifications on the structure of the protein can be evaluated using in silico modeling methods. This multi-step process has been termed DeFT for de-immunization of functional therapeutics. In this article we review the rationale for the approach, provide several retrospective examples that prove the approach in principle, and describe potential applications to therapeutic protein design. The demand for pre-clinical means of evaluating therapeutic proteins is expected to increase with the number of therapeutic proteins and monoclonal antibodies entering the pre-clinical pipeline. Examples provided offer some preliminary proof that the de-immunization approach may improve clinical outcomes.

Animals↗

PubMed2006

Dynamic modeling of alpha-synuclein aggregation for the sporadic and genetic forms of Parkinson's disease.

Excessive accumulation of alpha synuclein (a-syn) in the brain has been implicated in several degenerative neurological disorders, most notably Parkinson's disease. The aggregation of a-syn is the major component of intraneuronal inclusions, Lewy bodies, which are neuropathological features, observed in Parkinson's disease, Lewy body dementia, and other synucleopathies. Diverse cellular events can contribute to a-syn accumulation, aggregation, and to subsequent Lewy body formation. These factors include genetic mutations of synuclein, parkin, or the deubiquitinating enzyme, ubiquitin C-terminal hydrolase (UCH-L1), leading to reduced clearance of a-syn by the ubiquitin proteasomal pathway (UPP). Furthermore, intracellular insults include environmental factors and an age-related decrement in antioxidant defense systems that increase oxidative stress and can affect either the accumulation or clearance of a-syn. We have dynamically modeled a-syn processing in normal and in several disease states; focusing upon alterations in the aggregation and clearance of a-syn as influenced by the UPP and the oxidative stress pathways. Simulation of increased oxidative stress generates a free radical profile analogous to that reported in vivo following exposure to the neurotoxin, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Varying model parameters of oxidative stress, UPP dysfunction, or both pathways, simulate kinetics of a-syn that corresponds with the neuropathology described for the sporadic and genetic forms of Parkinson's disease. This in silico model provides a mathematical framework that enables kinetic appraisal of pathway components to better identify and validate important pharmacological targets.

Animals↗

PubMed2004

Using in vitro human tissues to predict pharmacokinetic properties.

Pressure on drug discovery teams to predict the success of drug candidates earlier in the drug discovery process has led to the development of in vitro assays using human tissues, cells or fluids that aid chemists and biologists in their decision-making process. The use of human material is often related to the species-specific nature of the enzymatic biotransformations or transport processes that are involved in drug bioavailability. In vitro assays have been developed to indicate liabilities in scaffolds relating to the absorption, distribution or metabolism of new chemical entities. These models have been applied not only to screening and ranking of potential drug candidates but also to the understanding of the mechanisms leading to in vivo pharmacokinetic outcomes. The use of these models is leading to the development of structure-ADME property relationships in a manner similar to classical structure-activity relationship development, and in the future this is expected to lead to in silico models for predicting physiological and pharmacological effects prior to experimentation.

Biological Transport↗

PubMed2003

Progress in simulation modelling for pharmacokinetics.

Simulation models for the prediction of pharmacokinetics in humans and other mammalian species, which are based on the physiology and mechanistic models of absorption, distribution, metabolism and elimination are reviewed. The structure of such models is explained with reference to papers describing the mathematical details and alternative representations of organ flow and distribution. Approaches to the modelling of more complex tissues such as tumours and the liver are also reviewed as well as some specific transport processes such as biliary secretion and methods of ADME property estimation by experimental and in silico models. Specific approaches to the modelling of gastro-intestinal transit are explained as is the extension of the approach to simulating drug-drug interactions following co-administration of more than one drug.

Absorption↗

PubMed2006

Estrogens, enzyme variants, and breast cancer: a risk model.

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed a mathematical model of mammary estrogen metabolism based on the conversion of 17beta-estradiol (E(2)) by the enzymes cytochrome P450 (CYP) 1A1 and CYP1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 into eight metabolites [i.e., two catechol estrogens, 2-hydroxyestradiol (2-OHE(2)) and 4-hydroxyestradiol (4-OHE(2)); three methoxyestrogens, 2-methoxyestradiol, 2-hydroxy-3-methoxyestradiol, and 4-methoxyestradiol; and three glutathione (SG)-estrogen conjugates, 2-OHE(2)-1-SG, 2-OHE(2)-4-SG, and 4-OHE(2)-2-SG]. When used with experimentally determined rate constants with purified enzymes, the model provides for a kinetic analysis of the entire metabolic pathway. The predicted concentration of each metabolite during a 30-minute reaction agreed well with the experimentally derived results. The model also enables simulation for the transient quinones, E(2)-2,3-quinone (E(2)-2,3-Q) and E(2)-3,4-quinone (E(2)-3,4-Q), which are not amenable to direct quantitation. Using experimentally derived rate constants for genetic variants of CYP1A1, CYP1B1, and COMT, we used the model to simulate the kinetic effect of enzyme polymorphisms on the pathway and identified those haplotypes generating the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E(2)-3,4-Q production. This in silico model integrates both kinetic and genomic data to yield a comprehensive view of estrogen metabolomics in the breast. The model offers the opportunity to combine metabolic, genetic, and lifetime exposure data in assessing estrogens as a breast cancer risk factor.

Area Under Curve↗

PubMedDate not supplied

[Pharmacology of Alzheimer's disease: where do we go from here?].

Ten years after the introduction of the first drug for the treatment of Alzheimer's disease, tacrine, it seems appropriate to reappraise the pharmacological processes of innovation in the field of research in dementia. The aim of this review is to pinpoint concrete improvements achieved in this field, in terms of experimental methods and clinical evaluation of the compounds, as well as the neurochemistry of the disease and cellular targets deserving of initial consideration. * The article first considers the use of animal models of Alzheimer's disease, which are classified according to two categories: animals with lesions of some neuronal pathways specifically implicated in clinical symptoms (i.e. lesions of the nucleus basalis of Meynert, the origin of cholinergic projections to the cortex underlying memory processes); and transgenic models, which are intended to reproduce some of the neuropathological hallmarks of Alzheimer's disease. Drugs can be tested in animals with such alterations for their effect on neuropathology, neurochemistry and behavioural disturbances. More recently, in silico models have been developed, which offer the possibility of simulating the pharmacodynamic effects of drugs in specific areas of the brain. These experiments are helpful in distinguishing purely symptomatic effects from disease-modifying effects, the latter being the ultimate goal of the modern pharmacology of dementia. * The second breakthrough considered in this article is the codification and standardisation of clinical methods for obtaining a more accurate and earlier diagnosis (the recent introduction of the concept of "Mild Cognitive Impairment", which includes patients who will later develop a true clinical dementia syndrome). In that respect, the determination of the biological markers of Alzheimer's disease (apolipoprotein E, amyloid substance, protein-tau, isoprostane) as well as progress in neuroimaging (functional positron emission tomography [fPET]-scan, single photon emission-computed tomography [SPECT], functional nuclear magnetic resonance [fNMR]) are discussed in terms of their potential as new tools in the early stages of drug development (surrogate markers). The methods used during the comparative clinical trials (phase III) have been elaborated and internationally standardised during the assessment of the different acetylcholinesterase inhibitors (AChE-I), with the knowledge that, since 1994, four of these have been officially approved: tacrine, donepezil, rivastigmine and galantamine; the same methods have been used for developing memantine, a recently-launched modulator of glutamatergic neurotransmission. The validated scales now take into consideration not only the cognitive dimensions of Alzheimer's disease but also the behavioural symptoms, with the introduction of the concept of BPSD (behavioural psychological symptoms of dementia). Some proposals to improve this clinical assessment of anti-dementia drugs are presented here. * The section of this article dealing with prospective issues considers the main pathways of interest in drug innovation and the elucidation of new targets for the future compounds. As well as their symptomatic effects on the different components of cognition, drugs should be neuroprotective and limit the lesions documented in Alzheimer's disease, with the aim of progressing far beyond the amyloid hypothesis (immunisation, beta-sheet breakers, secretase inhibitors). The field of excitotoxicity (which is mainly glutamate dependent) appears fruitful, because of the possibility of pharmacological intervention at the different steps in the excitotoxic process. All the new directions presented in this article support the concept of true disease-modifying agents. In conclusion, this prospective review should be considered as a guide in fostering drug innovation in Alzheimer's disease and related disorders and should help to decrease the gap existing between neuroscience and therapeutics.

Aged↗

PubMed2003

Quantitative analysis of Escherichia coli metabolic phenotypes within the context of phenotypic phase planes.

In silico models of Escherichia coli metabolism have been developed to predict metabolic behavior and propose experimentally testable hypotheses. However, a thorough assessment of the metabolic phenotype requires well-designed experimentation and reproducible experimental techniques. A method for the quantitative analysis of E. coli metabolism in vivo within the framework of in silico phenotypic phase plane analysis is presented. Using this approach, we have quantitatively studied E. coli metabolism in various environmental conditions and nutritional media. Our experimental methodology, in combination with steady-state metabolic models, can be used to study biological properties and evaluate the metabolic capabilities of microbes.

Aerobiosis↗

PubMed2004

Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates.

A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds.

Cytochrome P-450 CYP3A↗

PubMed2006

Predicted serum folate concentrations based on in vitro studies and kinetic modeling are consistent with measured folate concentrations in humans.

The nutritional quality of new functional or fortified food products depends on the bioavailability of the nutrient(s) in the human body. Bioavailability is often determined in human intervention studies by measurements of plasma or serum profiles over a certain time period. These studies are time and cost consuming and often appear to lack an optimal study design, leading to follow-up intervention trials. Therefore, an alternative approach is needed that will optimize the development of new products. This study describes an approach to predict human serum concentrations after the consumption of (fortified) food products. The concept is based on the integration of in vitro results with kinetic modeling. As a case study, human serum folate concentrations were predicted after the consumption of folate-fortified milk products for 4 wk. Oral bioavailability was investigated using a step-wise approach in which luminal bioaccessibility and intestinal absorption were independently evaluated. Subsequently, these in vitro data were integrated in a kinetic mathematical (in silico) model to predict serum folate concentrations after the intake of a single dose and during long-term consumption. This approach was evaluated in comparison to a human intervention study in which folic acid-fortified milk products were tested for their effect on serum folate concentrations. A high predictive quality of this alternative in vitro/in silico approach was demonstrated. Finally, this methodology was applied to predict serum folate concentrations after intake of different fortified milk products for 4 wk, showing its benefits for the development of new nutritional products.

Dairy Products↗

PubMed2006

Multicell tumor spheroids in photodynamic therapy.

BACKGROUND AND OBJECTIVES: Multicell spheroids (MCSs) represent a simple in vitro system ideally suited for studying the effects of a wide variety of investigational treatments including photodynamic therapy (PDT). STUDY DESIGN/MATERIALS AND METHODS: In the first section of this review study, an overview of the current literature on MCS in PDT will be presented. Knowledge of basic PDT parameters has been gained from numerous MCS studies, in particular, the mechanisms of sensitizer photobleaching have been elucidated. MCSs have also proven useful for the study of complex PDT treatment regimens including multiple treatments and combined therapies involving PDT and ionizing radiation or hyperthermia. The purpose of the second part of this review is to present results from recent studies in our laboratory aimed at developing MCS models suitable for investigating tumor cell invasion and angiogenesis-processes characteristic of high-grade gliomas. RESULTS AND CONCLUSION: To that end, progress has recently been made to develop a more accurate in vivo brain tumor model consisting of biopsy-derived human tumor spheroids implanted into the brains of immunodeficient rats. Finally, recent work suggests that computer simulations may prove useful to describe the growth of MCS and predict the effects of investigational therapies including PDT. Such in silico models have made a number of counterintuitive predictions that have been verified in vitro and, as such, could guide the development of improved therapeutics.

Animals↗

PubMed2006

A structural model of 20S immunoproteasomes: effect of LMP2 codon 60 polymorphism on expression, activity, intracellular localisation and insight into the regulatory mechanisms.

The immunoproteasome subunit low molecular weight protein 2 (LMP2) codon 60 polymorphism has been associated with autoimmune diseases. It has also been demonstrated to influence susceptibility to TNF-alpha-induced apoptosis in blood cells and proteasome activity in aged human brain. In the present study, an in silico model of immunoproteasome was used to examine the effect of the R60H polymorphism in the LMP2 subunit. The investigation of immunoproteasome expression, activity and intracellular localisation in an in vitro cellular model, namely lymphoblastoid cell lines, showed no major variations in functionality and amount, while a significant difference in antibody affinity was apparent. These data were integrated with previous results obtained in different tissues and combined with a structural model of the LMP2 polymorphism. Accordingly, we identified three prospective mechanisms that could explain the biological data for the polymorphism, such as modulation of the binding affinity of a putative non-catalytic modifier site on the external surface of the immunoproteasome core, or the modification of any channel between alpha and beta rings.

Antibodies, Monoclonal↗

PubMed2002

Models for profiling the potential QT prolongation risk of drugs.

The appearance of QT prolongation and arrhythmic events associated with a compound undergoing clinical trials can greatly hamper drug development programs. Assessing the risk of a compound during preclinical studies to cause this cardiotoxicity is thus critically important to the pharmaceutical industry. A wide variety of preclinical approaches exist to evaluate potential QT issues, including in vitro, in vivo and in silico (i.e., computer simulation) methods. We present an evaluation of recent reports implementing these techniques, with an emphasis on the linkage between drug-induced cardiac action potential changes and QT prolongation both in vitro and in silico. We conclude with a strategy that integrates in silico modeling with in vitro and in vivo experimentation to create a compelling package for assessing potential proarrhythmic risk of a compound.

Animals↗

PubMed2006

Two novel presenilin 1 gene mutations connected with frontotemporal dementia-like clinical phenotype: genetic and bioinformatic assessment.

Mutations in the amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2 (PSEN2) genes are associated with early-onset familial Alzheimer's disease (EOAD). There are several reports describing mutations in PSEN1 in cases with frontotemporal dementia (FTD). We identified two novel mutations in the PSEN1 gene: L226F and L424H. The first mutation was detected in a patient with a clinical diagnosis of FTD and a post-mortem diagnosis of AD. The second mutation is connected with a clinical phenotype of variant AD with strong FTD signs. In silico modeling revealed that the mutations, as well as mutations used for comparison (F177L and L424R), change the local structure, stability and/or properties of the transmembrane regions of the presenilin 1 protein (PS1). In contrast, a silent non-synonymous substitution F175S is eclipsed by external residues and has no influence on PS1 interfacial surface. We suggest that in silico analysis of PS1 substitutions can be used to characterize novel PSEN1 mutations, to discriminate between silent polymorphisms and a potential disease-causing mutation. We also propose that PSEN1 mutations should be considered in FTD patients with no MAPT mutations.

Adult↗

PubMed2005

Multivariate modeling of cytochrome P450 3A4 inhibition.

In the early phases of current pharmaceutical research projects, huge numbers of compounds are tested on their biological activity with respect to a certain target by experimental or virtual screening campaigns. To reduce the attrition rate in later stages of a project, other relevant properties such as physicochemical and ADMET (absorption, distribution, metabolism, excretion, toxicity) properties should be assessed as early as possible in lead discovery and optimization. The present study describes the development of in silico models to predict the inhibition of human cytochrome P450 3A4 (CYP3A4) from calculated molecular descriptors. The models were trained and validated using a set of 967 structural diverse drug-like research compounds with an experimentally determined CYP3A4 inhibition potency (IC50 value) which was carefully split into a training and a test set. For classification models, the data sets were further subdivided into strong, medium, and weak inhibitors. Different descriptor sets were used to cover various aspects of molecular properties, including properties derived from the 2D structure, the interaction of the molecule with its environment, and properties derived from quantum-mechanical calculations. The descriptors were related to the CYP3A4 inhibition potency by multivariate data analysis methods such as partial least-squares projection to latent structures (PLS), PLS discriminant analysis (PLS-DA), and soft independent class modeling (SIMCA). The squared correlation between experimental and predicted IC50 values of the previously unseen test set compounds was Qext2=0.6 for the best PLS models, corresponding to a root mean squared error (RMSE) of RMSE=0.45 (logarithm of IC50). The best PLS-DA models were able to correctly classify more than 60% of the test set compounds, whereas almost no strong inhibitors were wrongly classified as weak inhibitors and vice versa. Furthermore, relevant molecular properties were identified which are closely related to the CYP3A4 inhibition potency of a compound. The results presented here are very encouraging since our models could, for instance, serve to flag problematic compounds or to guide further synthesis efforts.

Cytochrome P-450 CYP3A↗

PubMed2005

Antagonist and agonist binding models of the human gonadotropin-releasing hormone receptor.

G-protein-coupled receptors (GPCRs) constitute one of the most important classes of drug targets. Since the first high-resolution structure of a GPCR was determined by Palczewski and co-workers [K. Palczewski, T. Kumasaka, T. Hori, C.A. Behnke, H. Motoshima, B.A. Fox, I. Le Trong, D.C. Teller, T. Okada, R.E. Stenkamp, M. Yamamoto, M. Miyano, Crystal structure of rhodopsin: a G-protein-coupled receptor, Science 289 (2000) 739-745], development of in silico models of rhodopsin-like GPCRs could be rationally founded. In this work, we present a model of the human gonadotropin-releasing hormone receptor based on the rhodopsin structure. The transmembrane helices are modeled by homology, while the extra- and intra-cellular loops are modeled in such a way that experimentally determined interactions and microdomains (e.g., hydrophobic cores) are retained. We conclude that specifically tailored models, compared to more automatic approaches, have the benefit that known interactions are easily introduced early in the homology modeling. Furthermore, tailored models, although more tedious to construct, are better suited for drug lead finding and for compound optimization. To test the stability of the receptor, we performed a 1 ns molecular dynamics simulation. Moreover, we docked two agonists (native GnRH and Triptorelin, [dTrp(6)]-GnRH) and two antagonists (Cetrorelix, dNal(1)-dCpa(2)-dPal(3)-Ser(4)-Tyr(5)-dCit(6)-Leu(7)-Arg(8)-Pro(9)-dAla(10)), and the covalently constrained dicyclic decapeptide dicyclo(1,1'-5/4-10)[Ac-Glu(1)(Gly(1)')-dCpa(2)-dTrp(3)-Asp(4)-dbu(5)-dNal(6)-Leu(7)-Arg(8)-Pro(9)-dpr(10)-NH(2)] into the putative receptor binding site. The docked ligand conformations result in ligand-receptor interactions that are generally in good agreement with site-directed mutagenesis and ligand-binding studies presented in the literature. Our results indicate that the binding conformation of the antagonists differs from that of the agonists. This difference can be linked to the activation or inhibition of the receptor.

Binding Sites↗

PubMed2004

Modelling the impact of antigen kinetics on T-cell activation and response.

Cytotoxic T lymphocyte (CTL) responses are thought to be important for the control of many viral and other infections. Qualitative aspects of the CTL response, including the epitope specificity, affinity, and clonal composition, may affect the ability of T cells to mediate infection control. Although it is clear that the mode of introduction and the dose of antigen can affect these qualitative aspects of the response, little is understood of the mechanisms. We have developed an in silico model of the CTL response, which we use to study the impact of antigen dose, antigen kinetics and repeated antigen delivery on the response. The results suggest that recent observations on differences in response to killed antigen can be explained simply by differences in timing of T-cell activation. These findings may provide insight into how different vaccination strategies can quantitatively and qualitatively affect the outcome of the immune response.

Antigens, Viral↗

PubMed2005

Drug tissue distribution: study methods and therapeutic implications.

Current interest in studies on tissue distribution stems from the limited capacity to predict tissue concentrations and the pharmacological response from plasma drug levels, and from the limitations - both methodological and deontological- involved in doing so, especially in humans. In this review we carry out a comparative analysis of the methods used for studying tissue distribution, placing special focus on recently developed non-invasive methods for the research of tissue distribution in humans, such as positron emission tomography and nuclear magnetic resonance spectroscopy. We describe the strategies of tissue distribution pharmacokinetic analysis that have evolved from analysis based on compartment and physiological models to analyses based on spatial and fractal models and, mainly, pharmacokinetic-pharmacodynamic models. Model-independent analysis based on the use of mean transit times or deconvolution strategies has become a good alternative for the pharmacokinetic analysis of tissue distribution. The need to increase the selectivity of many drugs justifies the desire to gain further insight into the design of new analogues prodrugs and carrier systems that will guarantee the specific delivery of a given drug to a particular organ or tissue, optimising the response. In silico models for drug distribution have become a helpful tool in drug discovery and development. The therapeutic implications of drug tissue distribution are also analysed, special reference being made to antiretroviral therapy and antitumoural gene therapy, among others.

Animals↗

PubMed2002

Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field.

One of the conclusions drawn at the CASP4 meeting in Asilomar was that applying various force fields during refinement of template-based models tends to move predictions in the wrong direction, away from the experimentally determined coordinates. We have derived an all-atom force field aimed at protein and nucleotide optimization in vacuo (NOVA), which has been specifically designed to avoid this problem. NOVA resembles common molecular dynamics force fields but has been automatically parameterized with two major goals: (i) not to make high resolution X-ray structures worse and (ii) to improve homology models built by WHAT IF. Force-field parameters were not required to be physically correct; instead, they were optimized with random Monte Carlo moves in force-field parameter space, each one evaluated by simulated annealing runs of a 50-protein optimization set. Errors inherent to the approximate force-field equation could thus be canceled by errors in force-field parameters. Compared with the optimization set, the force field did equally well on an independent validation set and is shown to move in silico models closer to reality. It can be applied to modeling applications as well as X-ray and NMR structure refinement. A new method to assign force-field parameters based on molecular trees is also presented. A NOVA server is freely accessible at http://www.yasara.com/servers

Algorithms↗