The Transformer database: biotransformation of xenobiotics.

As the number of prescribed drugs is constantly rising, drug-drug interactions are an important issue. The simultaneous administration of several drugs can cause severe adverse effects based on interactions with the same metabolizing enzyme(s). The Transformer database (http://bioinformatics.charite.de/transformer) contains integrated information on the three phases of biotransformation (modification, conjugation and excretion) of 3000 drugs and >350 relevant food ingredients (e.g. grapefruit juice) and herbs, which are catalyzed by 400 proteins. A total of 100,000 interactions were found through text mining and manual validation. The 3D structures of 200 relevant proteins are included. The database enables users to search for drugs with a visual display of known interactions with phase I (Cytochrome P450) and phase II enzymes, transporters, food and herbs. For each interaction, PubMed references are given. To detect mutual impairments of drugs, the drug-cocktail tool displays interactions between selected drugs. By choosing the indication for a drug, the tool offers suggestions for alternative medications to avoid metabolic conflicts. Drug interactions can also be visualized in an interactive network view. Additionally, prodrugs, including their mechanisms of activation, and further information on enzymes of biotransformation, including 3D models, can be viewed.

Polymorphic cytochrome P450 enzymes (CYPs) and their role in personalized therapy.

The cytochrome P450 (CYP) enzymes are major players in drug metabolism. More than 2,000 mutations have been described, and certain single nucleotide polymorphisms (SNPs) have been shown to have a large impact on CYP activity. Therefore, CYPs play an important role in inter-individual drug response and their genetic variability should be factored into personalized medicine. To identify the most relevant polymorphisms in human CYPs, a text mining approach was used. We investigated their frequencies in different ethnic groups, the number of drugs that are metabolized by each CYP, the impact of CYP SNPs, as well as CYP expression patterns in different tissues. The most important polymorphic CYPs were found to be 1A2, 2D6, 2C9 and 2C19. Thirty-four common allele variants in Caucasians led to altered enzyme activity. To compare the relevant Caucasian SNPs with those of other ethnicities a search in 1,000 individual genomes was undertaken. We found 199 non-synonymous SNPs with frequencies over one percent in the 1,000 genomes, many of them not described so far. With knowledge of frequent mutations and their impact on CYP activities, it may be possible to predict patient response to certain drugs, as well as adverse side effects. With improved availability of genotyping, our data may provide a resource for an understanding of the effects of specific SNPs in CYPs, enabling the selection of a more personalized treatment regimen.

SynSysNet: integration of experimental data on synaptic protein-protein interactions with drug-target relations.

We created SynSysNet, available online at http://bioinformatics.charite.de/synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction proteomics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ∼1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50 000 compounds, as well as 5000 experimentally validated protein-protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein-protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given.

Drug cocktail optimization in chemotherapy of cancer.

BACKGROUND: In general, drug metabolism has to be considered to avoid adverse effects and ineffective therapy. In particular, chemotherapeutic drug cocktails strain drug metabolizing enzymes especially the cytochrome P450 family (CYP). Furthermore, a number of important chemotherapeutic drugs such as cyclophosphamide, ifosfamide, tamoxifen or procarbazine are administered as prodrugs and have to be activated by CYP. Therefore, the genetic variability of these enzymes should be taken into account to design appropriate therapeutic regimens to avoid inadequate drug administration, toxicity and inefficiency. OBJECTIVE: The aim of this work was to find drug interactions and to avoid side effects or ineffective therapy in chemotherapy. DATA SOURCES AND METHODS: Information on drug administration in the therapy of leukemia and their drug metabolism was collected from scientific literature and various web resources. We carried out an automated textmining approach. Abstracts of PubMed were filtered for relevant articles using specific keywords. Abstracts were automatically screened for antineoplastic drugs and their synonyms in combination with a set of human CYPs in title or abstract. RESULTS: We present a comprehensive analysis of over 100 common cancer treatment regimens regarding drug-drug interactions and present alternatives avoiding CYP overload. Typical concomitant medication, e.g. antiemetics or antibiotics is a preferred subject to improvement. A webtool, which allows drug cocktail optimization was developed and is publicly available on http://bioinformatics.charite.de/chemotherapy.

Identification of novel small molecules that inhibit protein-protein interactions between MAGE and KAP-1.

The Class I MAGE proteins are normally expressed only in developing germ cells but are often aberrantly expressed in malignancies, particularly melanoma, making them good therapeutic targets. MAGE proteins promote tumor survival by binding to the RBCC region of KAP-1 and suppressing p53. Although, suppression of MAGE expression, by RNA interference, relieves p53 suppression and inhibits tumor growth, its therapeutic uses are limited by lack of methods for systemic delivery of small interfering RNA. To overcome this barrier, we sought to discover chemical compounds that inhibit binding between MAGE and KAP-1 proteins. Based on previously published effects of MAGE suppression, we developed a strategy for screening a small molecule library based on selective death of MAGE positive cells, activation of p53 and lack of caspase activity. We screened the Maybridge HitFinder library of compounds and eight compounds fulfilled these criteria. Seven of these compounds interfered with co-precipitation of MAGE and KAP-1, and three interfered with binding of MAGE and KAP-1 in a mammalian two hybrid assay. We now report identification of three potential compounds that interfere with MAGE/KAP-1 binding and can be developed as novel chemo-therapeutic agents for treatment of advanced melanoma and other cancers.