Anton Franz Josef Fliri

Drug effects viewed from a signal transduction network perspective.

Understanding how drugs affect cellular network structures and how resulting signals are translated into drug effects holds the key to the discovery of medicines. Herein we examine this cause-effect relationship by determining protein network structures associated with the generation of specific in vivo drug-effect patterns. Medicines having similar in vivo pharmacology have been identified by a comparison of drug-effect profiles of 1320 medicines. Protein network positions reached by these medicines were ascertained by examining the coinvestigation frequency of these medicines and 1179 protein network constituents in millions of scientific investigations. Interestingly, medicine associations obtained by comparing by drug-effect profiles mirror those obtained by comparing drug-protein coinvestigation frequency profiles, demonstrating that these drug-protein reachability profiles are relevant to in vivo pharmacology. By using protein associations obtained in these investigations and independent, curated protein interaction information, drug-mediated protein network topology models can be constructed. These protein network topology models reveal that drugs having similar pharmacology profiles reach similar discrete positions in cellular protein network systems and provide a network view of medicine cause-effect relationships.

Synthesis, sar and pharmacology of CP-293,019 : A potent, selective dopamine D4 receptor antagonist

A series of novel, potent and selective pyrido[1,2-a]pyrazine dopamine D4 receptor antagonists are reported including CP-293,019 (D4 Ki = 3.4 nM, D2 Ki > 3,310 nM), which also inhibits apomorphine-induced hyperlocomotion in rats after oral dosing.

Analysis of system structure-function relationships.

Preclinical pharmacology studies conducted with experimental medicines currently focus on assessments of drug effects attributed to a drugs putative mechanism of action. The high failure rate of medicines in clinical trials, however, underscores that the information gathered from these studies is insufficient for forecasting drug effect profiles actually observed in patients. Improving drug effect predictions and increasing success rates of new medicines in clinical trials are some of the key challenges currently faced by the pharmaceutical industry. Addressing these challenges requires development of new methods for capturing and comparing “system‐wide” structure–effect information for medicines at the cellular and organism levels. The current investigation describes a strategy for moving in this direction by using six different descriptor sets for examining the relationship between molecular structure and broad effect information of 1064 medicines at the cellular and the organism level. To compare broad drug effect information between different medicines, information spectra for each of the 1064 medicines were created, and the similarity between information spectra was determined through hierarchical clustering. The structure–effect relationships ascertained through these comparisons indicate that information spectra similarity obtained through preclinical ligand binding experiments using a model proteome provide useful estimates for the broad drug effect profiles of these 1064 medicines in organisms. This premise is illustrated using the ligand binding profiles of selected medicines in the dataset as biomarkers for forecasting system‐wide effect observations of medicines that were not included in the incipient 1064‐medicine analysis.

The discovery of potent and selective dopamine D4 receptor antagonists

The identification of a novel dopamine receptor subtype, referred to as the D4 receptor, which binds the atypical antipsychotic drug clozapine with high potency, has led to the initiation of a drug discovery program that aims to find novel inhibitors of this receptor subtype. A selective screening strategy was utilized, in which 4500 compounds chosen on the basis of structural similarities to known biogenic amine receptor antagonists were tested against both the D4 and D2 dopamine receptor subtypes. A potent D4-selective compound was discovered.