Perspectives on the past, present and future of computer prediction in toxicology.
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Computer prediction of adaptive bone remodelling around noncemented femoral prostheses: the relationship between damage-based and strain-based algorithms.
Several mathematical models to predict tissue adaptation have been derived since Julius Wolff proposed a function-form relationship for bone. These can be formulated as computational procedures (algorithms) to predict bone adaptation around implants. The objective of this paper was to further develop the damage-adaptive algorithm, to test its validity, and to determine the relationship between it and algorithms based on strain energy. This was achieved using finite element models of the proximal femur, one for the intact case and another for the case where a noncemented hip prosthesis is implanted. The finite models were generated using CT scan data. Initial bone resorption patterns around a femoral prosthesis following total hip arthroplasty were computed for both damage-adaptive and strain-adaptive adaptation rules. It is found that the damage-adaptive algorithm can successfully predict the bone's adaptive behaviour in response to altered mechanical loading provided that account is taken of the nonlinear nature of damage accumulation. Predictions are made using a strain energy stimulus for comparison with the damage stimulus, and a theoretical relationship between the two is proposed. It is shown that an advantage of the damage approach over the strain-based approach is that the nonlinearity required to replicate clinically observed resorption patterns can be derived theoretically, whereas for strain-adaptive remodelling, empirical relationships are assumed.
Adaptation, Physiological↗
Starts of bacterial genes: estimating the reliability of computer predictions.
Exact mapping of gene starts is an important problem in the computer-assisted functional analysis of newly sequenced prokaryotic genomes. We describe an algorithm for finding ribosomal binding sites without a learning sample. This algorithm is particularly useful for analysis of genomes with little or no experimentally mapped genes. There is a clear correlation between the ribosomal binding site (RBS) properties of a given genome and the potential gene start prediction accuracy. This correlation is of considerable predictive power and may be useful for estimating the expected success of future genome analysis efforts. We also demonstrate that the RBS properties depend on the phylogenetic position of a genome.
Algorithms↗
Computational prediction of methylation status in human genomic sequences.
Epigenetic effects in mammals depend largely on heritable genomic methylation patterns. We describe a computational pattern recognition method that is used to predict the methylation landscape of human brain DNA. This method can be applied both to CpG islands and to non-CpG island regions. It computes the methylation propensity for an 800-bp region centered on a CpG dinucleotide based on specific sequence features within the region. We tested several classifiers for classification performance, including K means clustering, linear discriminant analysis, logistic regression, and support vector machine. The best performing classifier used the support vector machine approach. Our program (called hdfinder) presently has a prediction accuracy of 86%, as validated with CpG regions for which methylation status has been experimentally determined. Using hdfinder, we have depicted the entire genomic methylation patterns for all 22 human autosomes.
Algorithms↗
Computational prediction of miRNAs in Arabidopsis thaliana.
MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in animals and plants. Comparative genomic computational methods have been developed to predict new miRNAs in worms, flies, and humans. Here, we present a novel single genome approach for the detection of miRNAs in Arabidopsis thaliana. This was initiated by producing a candidate miRNA-target data set using an algorithm called findMiRNA, which predicts potential miRNAs within candidate precursor sequences that have corresponding target sites within transcripts. From this data set, we used a characteristic divergence pattern of miRNA precursor families to select 13 potential new miRNAs for experimental verification, and found that corresponding small RNAs could be detected for at least eight of the candidate miRNAs. Expression of some of these miRNAs appears to be under developmental control. Our results are consistent with the idea that targets of miRNAs encompass a wide range of transcripts, including those for F-box factors, ubiquitin conjugases, Leucine-rich repeat proteins, and metabolic enzymes, and that regulation by miRNAs might be widespread in the genome. The entire set of annotated transcripts in the Arabidopsis genome has been run through find MiRNA to yield a data set that will enable identification of potential miRNAs directed against any target gene.
Algorithms↗
Optimizing heat shock protein expression induced by prostate cancer laser therapy through predictive computational models.
Thermal therapy efficacy can be diminished due to heat shock protein (HSP) induction in regions of a tumor where temperatures are insufficient to coagulate proteins. HSP expression enhances tumor cell viability and imparts resistance to chemotherapy and radiation treatments, which are generally employed in conjunction with hyperthermia. Therefore, an understanding of the thermally induced HSP expression within the targeted tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of the overall tissue response. A treatment planning computational model capable of predicting the temperature, HSP27 and HSP70 expression, and damage fraction distributions associated with laser heating in healthy prostate tissue and tumors is presented. Measured thermally induced HSP27 and HSP70 expression kinetics and injury data for normal and cancerous prostate cells and prostate tumors are employed to create the first HSP expression predictive model and formulate an Arrhenius damage model. The correlation coefficients between measured and model predicted temperature, HSP27, and HSP70 were 0.98, 0.99, and 0.99, respectively, confirming the accuracy of the model. Utilization of the treatment planning model in the design of prostate cancer thermal therapies can enable optimization of the treatment outcome by controlling HSP expression and injury.
Animals↗
Computer prediction of possible toxic action from chemical structure: an update on the DEREK system.
Computer-based assessment of potential toxicity has become increasingly popular in recent years. The knowledge-base system DEREK is developed under the guidance of a multinational Collaborative Group of expert toxicologists and provides a qualitative approach to toxicity prediction. Major developments of the DEREK program and knowledge-base have taken place in the last 3 years. Program developments include improvements in both the user interface and data processing. Work on the knowledge-base has concentrated on the areas of genotoxicity and skin sensitisation. DEREK's predictive capabilities for these toxicological end-points has been demonstrated. In addition to the continued expansion of the knowledge-base, a number of enhancements are planned in the DEREK program. In particular, work is in progress to develop further DEREK's ability to report the reasoning behind its predictions.
Animal Testing Alternatives↗
Computer prediction of possible toxic action from chemical structure; the DEREK system.
1. The development of DEREK, a computer-based expert system (derived from the LHASA chemical synthesis design program) for the qualitative prediction of possible toxic action of compounds on the basis of their chemical structure is described. 2. The system is able to perceive chemical sub-structures within molecules and relate these to a rulebase linking the sub-structures with likely types of toxicity. 3. Structures can be drawn in directly at a computer graphics terminal or retrieved automatically from a suitable in-house database. 4. The system is intended to aid the selection of compounds based on toxicological considerations, or separately to indicate specific toxicological properties to be tested for early in the evaluation of a compound, so saving time, money and some laboratory animals and resources.
Animals↗
How to acquire new biological activities in old compounds by computer prediction.
Due to the directed way of testing chemical compounds' in drug research and development many projects fail because serious adverse effects and toxicity are discovered too late, and many existing prospective activities remain unstudied. Evaluation of the general biological potential of molecules is possible using a computer program PASS that predicts more than 780 pharmacological effects, mechanisms of action, mutagenicity, carcinogenicity, etc. on the basis of structural formulae of compounds, with average accuracy approximately 85%. PASS applications to both databases of available samples included hundreds of thousands compounds, and small collections of compounds synthesized by separate medicinal chemists are described. It is shown that 880 compounds from Prestwick chemical library represent a very diverse pharmacological space. New activities can be found in existing compounds by prediction. Therefore, on this basis, the selection of compounds with required and without unwanted properties is possible. Even when PASS cannot predict very new activities, it may recognize some unwanted actions at the early stage of R&D, providing the medicinal chemist with the means to increase the efficiency of projects.
Computer Simulation↗
Computational prediction of oral drug absorption based on absorption rate constants in humans.
Models for predicting oral drug absorption kinetics were developed by correlating absorption rate constants in humans (K(a)) with computational molecular descriptors. The K(a) values of a set of 22 passively absorbed drugs were derived from human plasma time-concentration profiles using a deconvolution approach. The K(a) values correlated well with experimental values of fraction of dose absorbed in humans (FA), better than the values of human jejunal permeability (P(eff)) which have previously been used to assess the in vivo absorption kinetics of drugs. The relationships between the K(a) values of the 22 structurally diverse drugs and computational molecular descriptors were established with PLS analysis. The analysis showed that the most important parameters describing log K(a) were polar surface area (PSA), number of hydrogen bond donors (HBD), and log D at a physiologically relevant pH. Combining log D at pH 6.0 with PSA or HBD resulted in models with Q(2) and R(2) values ranging from 0.74 to 0.76. An external data set of 169 compounds demonstrated that the models were able to predict K(a) values that correlated well with experimental FA values. Thus, it was shown that, using a combination of only two computational molecular descriptors, it is possible to predict with good accuracy the K(a) value for a new drug candidate.
Administration, Oral↗
Computational prediction of cancer-gene function.
Most cancer genes remain functionally uncharacterized in the physiological context of disease development. High-throughput molecular profiling and interaction studies are increasingly being used to identify clusters of functionally linked gene products related to neoplastic cell processes. However, in vivo determination of cancer-gene function is laborious and inefficient, so accurately predicting cancer-gene function is a significant challenge for oncologists and computational biologists alike. How can modern computational and statistical methods be used to reliably deduce the function(s) of poorly characterized cancer genes from the newly available genomic and proteomic datasets? We explore plausible solutions to this important challenge.
Computational Biology↗
Computer prediction of drug resistance mutations in proteins.
Drug resistance is of increasing concern in the treatment of infectious diseases and cancer. Mutation in drug-interacting disease proteins is one of the primary causes for resistance particularly against anti-infectious drugs. Prediction of resistance mutations in these proteins is valuable both for the molecular dissection of drug resistance mechanisms and for predicting features that guide the design of new agents to counter resistant strains. Several protein structure- and sequence-based computer methods have been explored for mechanistic study and prediction of resistance mutations. These methods and their usefulness are reviewed here.
Computer Simulation↗
Computational prediction of proteotypic peptides for quantitative proteomics.
Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).
Algorithms↗
Experimental measurements and computer predictions of copper complex formation by soluble soil organic matter.
Complexing of metals by organic matter can strongly influence their biological activity in the environment. The extent of copper complex formation by soluble organic matter extracted from an organic soil, a clay, and two sandy loams, was measured under identical conditions using two independent experimental methods. The results in every case fitted equations similar to Langmuir two-surface isotherms, but the values of complexing capacity and complexing strength were not the same for the organic matter from the four soils, and so were unsuitable for use in computer programs intended to predict concentrations of individual copper species in soil solutions. These concentrations can, however, be predicted by an empirical relationship between free and total copper at a constant pH and ionic strength.
Journal Article↗
Computer prediction of RNA structure.
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Algorithms↗
[Computational predictions of transcription factor binding sites].
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Animals↗
Computational prediction of the plasma protein-binding percent of diverse pharmaceutical compounds.
A nonlinear regression analysis has been applied to develop a new method to predict the plasma protein-binding percent of structurally diverse pharmaceutical compounds. The analysis included over 300 launched drugs with experimental human plasma protein binding percent data. These drugs were classified according to protonation state and pharmacophore features. The correlation formula for each class is a simple sigmoidal function of variable LogP or LogD. A correlation formula of variable LogD at pH 7.4 with a good correlation coefficient (R-squared = 0.803) was obtained for neutral and basic drugs, with the exception of zwitterions. A correlation formula using LogP as variable for acidic drugs with one of the specific pharmacophore features gave a good correlation coefficient (R-squared = 0.786). The method was verified using the protein binding data of 20 compounds that had not been included in the data set to configure the formulas. The correlation coefficient (R-squared) between the experimental and predicted protein binding percent was 0.830. In conclusion, the method developed and described in this report can provide precise and useful prediction of plasma protein binding percent for new drug candidates.
Blood Proteins↗
A radiographic analysis of computer prediction in conjunction with orthognathic surgery.
This retrospective study analysed the accuracy and reliability of predictions generated in patients treated with orthognathic surgery by comparing Quick Ceph Image software (Quick Ceph Image Pro version 3.0) predictions with post-surgical lateral cephalographs. Pre- and post-surgical lateral cephalographs of 28 adult patients (12 males and 16 females) were scanned into the computer and 28 landmarks were identified and digitized. Digitization error was assessed from repeated digitization. Fourteen measurements of the predicted and actual postsurgical hard tissue landmarks were compared using Student's t-test. Results showed a good correlation between repeated digitization for all measurements. Student's t-test indicated that 10 of the 14 measurements showed no statistically significant differences. Only the ANB (P=0.008), FMA (P=0.001), SN-Mxl (P=0.03) and Wit's (P=0.0001) showed statistically significant differences between the predicted and actual measurements. However only the Wit's showed clinical significant differences between the two measurements. Caution still must remain as the surgeon may not achieve his planned position in an individual patient. In some cultures there may also be medico-legal implications of these predictions.
Adolescent↗