Ulf Norinder

Bio: Ulf Norinder is an academic researcher from Stockholm University. The author has contributed to research in topics: Quantitative structure–activity relationship & Applicability domain. The author has an hindex of 39, co-authored 152 publications receiving 5184 citations. Previous affiliations of Ulf Norinder include Uppsala University & Pharmacia.

Identification of novel specific and general inhibitors of the three major human ATP-binding cassette transporters P-gp, BCRP and MRP2 among registered drugs.

Abstract: To study the inhibition patterns of the three major human ABC transporters P-gp (ABCB1), BCRP (ABCG2) and MRP2 (ABCC2), using a dataset of 122 structurally diverse drugs. Inhibition was investigated in cellular and vesicular systems over-expressing single transporters. Computational models discriminating either single or general inhibitors from non-inhibitors were developed using multivariate statistics. Specific (n = 23) and overlapping (n = 19) inhibitors of the three ABC transporters were identified. GF120918 and Ko143 were verified to specifically inhibit P-gp/BCRP and BCRP in defined concentration intervals, whereas the MRP inhibitor MK571 was revealed to inhibit all three transporters within one log unit of concentration. Virtual docking experiments showed that MK571 binds to the ATP catalytic site, which could contribute to its multi-specific inhibition profile. A computational model predicting general ABC inhibition correctly classified 80% of both ABC transporter inhibitors and non-inhibitors in an external test set. The inhibitor specificities of P-gp, BCRP and MRP2 were shown to be highly overlapping. General ABC inhibitors were more lipophilic and aromatic than specific inhibitors and non-inhibitors. The identified specific inhibitors can be used to delineate transport processes in complex experimental systems, whereas the multi-specific inhibitors are useful in primary ABC transporter screening in drug discovery settings.

Classification of Inhibitors of Hepatic Organic Anion Transporting Polypeptides (OATPs): Influence of Protein Expression on Drug - Drug Interactions

Abstract: The hepatic organic anion transporting polypeptides (OATPs) influence the pharmacokinetics of several drug classes and are involved in many clinical drug–drug interactions. Predicting potential interactions with OATPs is, therefore, of value. Here, we developed in vitro and in silico models for identification and prediction of specific and general inhibitors of OATP1B1, OATP1B3, and OATP2B1. The maximal transport activity (MTA) of each OATP in human liver was predicted from transport kinetics and protein quantification. We then used MTA to predict the effects of a subset of inhibitors on atorvastatin uptake in vivo. Using a data set of 225 drug-like compounds, 91 OATP inhibitors were identified. In silico models indicated that lipophilicity and polar surface area are key molecular features of OATP inhibition. MTA predictions identified OATP1B1 and OATP1B3 as major determinants of atorvastatin uptake in vivo. The relative contributions to overall hepatic uptake varied with isoform specificities of the inhi...

Experimental and Computational Screening Models for the Prediction of Intestinal Drug Absorption

Abstract: The aim of this study was to devise experimental protocols and computational models for the prediction of intestinal drug permeability. Both the required experimental and computational effort and the accuracy and quality of the resulting predictions were considered. In vitro intestinal Caco-2 cell monolayer permeabilities were determined both in a highly accurate experimental setting (Pc) and in a faster, but less accurate, mode (Papp). Computational models were built using four different principles for generation of molecular descriptors (atom counts, molecular mechanics calculations, fragmental, and quantum mechanics approaches) and were evaluated for their ability to predict intestinal membrane permeability. A theoretical deconvolution of the polar molecular surface area (PSA) was also performed to facilitate the interpretation of this composite descriptor and allow the calculation of PSA in a simplified and fast mode. The results indicate that it is possible to predict intestinal drug permeability fro...

Prediction of ADMET Properties

Abstract: This Review describes some of the approaches and techniques used today to derive in silico models for the prediction of ADMET properties. The article also discusses some of the fundamental requirements for deriving statistically sound and predictive ADMET relationships as well as some of the pitfalls and problems encountered during these investigations. It is the intension of the authors to make the reader aware of some of the challenges involved in deriving useful in silico ADMET models for drug development.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

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Molecular properties that influence the oral bioavailability of drug candidates.

Abstract: Oral bioavailability measurements in rats for over 1100 drug candidates studied at SmithKline Beecham Pharmaceuticals (now GlaxoSmithKline) have allowed us to analyze the relative importance of molecular properties considered to influence that drug property. Reduced molecular flexibility, as measured by the number of rotatable bonds, and low polar surface area or total hydrogen bond count (sum of donors and acceptors) are found to be important predictors of good oral bioavailability, independent of molecular weight. That on average both the number of rotatable bonds and polar surface area or hydrogen bond count tend to increase with molecular weight may in part explain the success of the molecular weight parameter in predicting oral bioavailability. The commonly applied molecular weight cutoff at 500 does not itself significantly separate compounds with poor oral bioavailability from those with acceptable values in this extensive data set. Our observations suggest that compounds which meet only the two cr...