Absorption, Distribution, Metabolism, Excretion Research Articles

A phase I Study with oral SU5416 in patients with advanced solid tumors: A drug inducing its clearance

Vascular endothelial growth factor (VEGF) is a potent stimulant of angiogenesis. SU5416, is a small molecule tyrosine kinase inhibitor, and a potent inhibitor of VEGF-mediated Flk-1 receptor signaling. Intravenous agent SU5416 has shown evidence of biological activity against a variety of tumor types. The current intravenous dosing regimen is not optimal for long-term administration, which is needed for optimal efficacy. The aim of this study was to evaluate the safety profile and pharmacokinetics of a Nanocrystal Colloidal Dispersion (NCD) SU5416 formulation in humans. Patients with advanced and/or metastatic solid organ tumors were included in the trial; various SU5416 regimens were tested for tolerability, safety and were evaluated concerning pharmacokinetics. The results of this study indicate that induction of clearance after oral dosing of NCD SU5416 in humans occurs and is greater than following i.v. administration. It has been confirmed that SU5416 is a high clearance compound, also as an oral NCD formulation. The NCD formulation was well tolerated, but no effective drug serum levels could be achieved. These data help to understand the ADME (Absorption, Distribution, Metabolism, Excretion) properties of indoline chemical class compounds. The lessons learned should be applied in the development of next generation indoline anti-angiogenic and anti-tumor compounds.

In Silico ADME Prediction: Data Sets and Models

The models available in the literature for the in silico prediction of ADME (absorption, distribution, metabolism, excretion) properties, as well as the data sets used to derive them, are reviewed. Special emphasis is given to describe the latest and most complete models, with the largest and most diverse data sets. Models for human intestinal absorption, oral bioavailability, plasma protein binding, blood-brain barrier permeation, Pglycoprotein substrates and inhibitors, and metabolism are reviewed and discussed. An attempt is made to describe the general picture emerging from each set of models when possible, as well as the issues remaining to address in the different areas for future work. These models are an example of the utility of models and computer simulations for the prediction of pharmacokinetics. Keywords: in silico adme prediction, qsar, human intestinal absorption, bioavailability, plasma protein binding, bloodbrain barrier permeation, p-glycoprotein, metabolism

The value of alternative testing for neurotoxicity in the context of regulatory needs

Detection and characterisation of chemical-induced toxic effects in the central and peripheral nervous system represent a major challenge for employing newly developed technologies in the field of neurotoxicology. Precise cellular predictive test batteries for chemical-induced neurotoxicity are increasingly important for regulatory decision making, but also the most efficient way to keep costs and time of testing within a reasonable margin. Current in vivo test methods are based on behavioural and sensory perturbations coupled with routine histopathological investigations. In spite of the empirical usefulness of these tests, they are not always sensitive enough and often, they do not provide information that facilitates a detailed understanding of potential mechanisms of toxicity, thus enabling predictions. In general, such in vivo tests are unsuitable for screening large number of agents. One way to meet the need for more powerful and comprehensive tests via an extended scientific basis is to study neurotoxicity in specific cell types of the brain and to derive generalised mechanisms of action of the toxicants from such series of experiments. Additionally, toxicokinetic models are to be developed in order to give a rough account for the whole absorption, distribution, metabolism, excretion (ADME) process including the blood–brain barrier (BBB). Therefore, an intensive search for the development of alternative methods using animal and human-based in vitro and in silico models for neurotoxic hazard assessment is appropriate. In particular, neurotoxicology represents one of the major challenges to the development of in vitro systems, as it has to account also for heterogeneous cell interactions of the brain which require new biochemical, biotechnological and electrophysiological profiling methods for reliable alternative ways with a high throughput.

Which in vitro Screens Guide the Prediction of Oral Absorption and Volume of Distribution?

The development of medium to high-throughput in vitro screening of ADME (Absorption, Distribution, Metabolism, Excretion) properties has been the reply to higher demands on drug metabolism scientists to cope with progress in chemistry and biology. Two areas will be discussed here, namely screens for oral absorption and for volume of distribution. The prediction of these human pharmacokinetic parameters can be based on proper combination of simple physicochemical measurements. In the future in vitro screens most likely will be combined with in silico assessments of various ADME properties leading to the concept of in combo screening in drug discovery.

Mass Spectrometry in Drug Discovery: A Current Review

Drug discovery in the pharmaceutical industry has shown great demands for screening absorption, distribution, metabolism, excretion (ADME) and pharmacokinetics (PK) in guiding the selection of lead candidate compounds. Determination of ADME/PK properties of new chemical entities (NCE) in early drug discovery should allow defects to be corrected prior to time-consuming and expensive preclinical and clinical development stages. Mass spectrometry has evolved to become an irreplaceable technology in all types of drug discovery applications because of its high sensitivity, speed, selectivity, versatility, and ease of automation. This review will include current mass spectrometric techniques and applications in drug discovery, as well as future prospects.

Usefulness of Sweat Testing for the Detection of Cannabis Smoke

Several reports have demonstrated that sweat is a suitable alternative biological matrix for monitoring recent drug use (1)(2). This is based on the assumption that, in the context of the absorption/distribution/metabolism/excretion (ADME) cycle of drugs, a small but sufficient fraction of a drug is excreted in sweat and can be tested. The passage of lipid-soluble compounds from blood to other fluids or matrices is regulated by the substance’s p K a and by the pH of the other fluids or matices. A modified version of the Henderson–Hasselbach equation, which uses the p K a and the pH, allows theoretical calculation of the fluid-to-plasma concentration ratio (F/P ratio) (3). Drugs are generally incorporated into sweat by passive diffusion because of a concentration gradient in which only the free fraction of drug (unbound to proteins) diffuses through lipid membranes from plasma to sweat. Furthermore, because under normal conditions sweat, with a mean pH of 6.3, is more acidic than blood, basic drugs tend to accumulate in sweat. Two approaches are currently used in testing for drugs in sweat. The first is aimed at detection of recent use of drugs (<24 h) and involves only collection of sweat at a point in time. An immunochromatographic test of the sample then provides a qualitative result (4), or drugs in sweat collected on a cotton wipe can be extracted and subjected to confirmatory analysis (5). This approach is mainly oriented to identify individuals who are under the influence of drugs. The second approach is based on patch technology and allows monitoring of illicit drug use for time windows wider than those provided …

Towards integrated ADME prediction: past, present and future directions for modelling metabolism by UDP-glucuronosyltransferases

Undesirable absorption, distribution, metabolism, excretion (ADME) properties are the cause of many drug development failures and this has led to the need to identify such problems earlier in the development process. This review highlights computational (in silico) approaches that have been used to identify the characteristics of ligands influencing molecular recognition and/or metabolism by the drug-metabolising enzyme UDP-gucuronosyltransferase (UGT). Current studies applying pharmacophore elucidation, 2D-quantitative structure metabolism relationships (2D-QSMR), 3D-quantitative structure metabolism relationships (3D-QSMR), and non-linear pattern recognition techniques such as artificial neural networks and support vector machines for modelling metabolism by UGT are reported. An assessment of the utility of in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters highlights the benefit of using multiple pharmacophores and also non-linear techniques for classification. Some of the challenges facing the development of generalisable models for predicting metabolism by UGT, including the need for screening of more diverse structures, are also outlined.

Synthesis, biological activity and molecular modeling studies of novel COX-1 inhibitors

Synthesis of new potential COX-1 and/or COX-2 inhibitors, derivatives of 1,1-di-(3-carboxyphenyl)ethane, their biological activity, docking results on COX-1 enzyme and absorption, distribution, metabolism, excretion (ADME) properties are presented. In addition to known interactions between ketoprofen and ibuprofen, leading NSAID agents and COX-1 active site, the possibility of formation of additional interactions is explored. Interactions with Ala527, and with one of the water molecules situated within the active site are identified. Molecular mechanics and DFT calculations for studied compounds have revealed free rotation around two central bonds (C 1–C 3′ and C 1–C 3″), making them flexible, thus easier to enter and adjust to the active site. Further modifications of core structure have been undertaken to optimize biological activity and ADME properties. As a result, two of the compounds are indicated as novel COX-1 inhibitors.

The Role of ABC Transporters in Drug Resistance, Metabolism and Toxicity

ATP Binding Cassette (ABC) transporters form a special family of membrane proteins, characterized by homologous ATP-binding, and large, multispanning transmembrane domains. Several members of this family are primary active transporters, which significantly modulate the absorption, metabolism, cellular effectivity and toxicity of pharmacological agents. This review provides a general overview of the human ABC transporters, their expression, localization and basic mechanism of action. Then we shortly deal with the human ABC transporters as targets of therapeutic interventions in medicine, including cancer drug resistance, lipid and other metabolic disorders, and even gene therapy applications. We place a special emphasis on the three major groups of ABC transporters involved in cancer multidrug resistance (MDR). These are the classical P-glycoprotein (MDR1, ABCB1), the multidrug resistance associated proteins (MRPs, in the ABCC subfamily), and the ABCG2 protein, an ABC half-transporter. All these proteins catalyze an ATP-dependent active transport of chemically unrelated compounds, including anticancer drugs. MDR1 (P-glycoprotein) and ABCG2 preferentially extrude large hydrophobic, positively charged molecules, while the members of the MRP family can extrude both hydrophobic uncharged molecules and water-soluble anionic compounds. Based on the physiological expression and role of these transporters, we provide examples for their role in Absorption-Distribution-Metabolism-Excretion (ADME) and toxicology, and describe several basic assays which can be applied for screening drug interactions with ABC transporters in the course of drug research and development.

A New Approach to Predict the Biological Activity of Molecules Based on Similarity of Their Interaction Fields and the logP and logD Values: Application to Auxins.

The activity of a biological compound is dependent both on specific binding to a target receptor and its ADME (Absorption, Distribution, Metabolism, Excretion) properties. A challenge to predict biological activity is to consider both contributions simultaneously in deriving quantitative models. We present a novel approach to derive QSAR models combining similarity analysis of molecular interaction fields (MIFs) with prediction of logP and/or logD. This new classification method is applied to a set of about 100 compounds related to the auxin plant hormone. The classification based on similarity of their interaction fields is more successful for the indole than the phenoxy compounds. The classification of the phenoxy compounds is however improved by taking into account the influence of the logP and/or the logD values on biological activity. With the new combined method, the majority (8 out of 10) of the previously misclassified derivatives of phenoxy acetic acid are classified in accord with their bioassay...

A new approach to predict the biological activity of molecules based on similarity of their interaction fields and the logP and logD values: application to auxins.

The activity of a biological compound is dependent both on specific binding to a target receptor and its ADME (Absorption, Distribution, Metabolism, Excretion) properties. A challenge to predict biological activity is to consider both contributions simultaneously in deriving quantitative models. We present a novel approach to derive QSAR models combining similarity analysis of molecular interaction fields (MIFs) with prediction of logP and/or logD. This new classification method is applied to a set of about 100 compounds related to the auxin plant hormone. The classification based on similarity of their interaction fields is more successful for the indole than the phenoxy compounds. The classification of the phenoxy compounds is however improved by taking into account the influence of the logP and/or the logD values on biological activity. With the new combined method, the majority (8 out of 10) of the previously misclassified derivatives of phenoxy acetic acid are classified in accord with their bioassays. The recently determined crystal structure of the auxin-binding protein 1 (ABP1) enabled validation of our approach. The results of docking a few auxin related compounds with different biological activity to ABP1 correlate well with the classification based on similarity of MIFs only. Biological activity is, however, better predicted by a combined similarity of MIFs + logP/logD approach.

ADMET in silico modelling: towards prediction paradise?

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.

In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values.

We have investigated whether three important ADME (absorption, distribution, metabolism, excretion) related properties (aqueous solubility, human plasma protein binding, and human volume of distribution at steady-state) can be predicted from chemical structure alone if only the predicted predominant ionisation state and lipophilicity (calculated logP [P = octanol-water partition coefficient]) are considered. A simple, fast method for the in silico prediction of aqueous solubility of predominantly uncharged compounds has been developed, while some potential is shown for the prediction of predominantly charged or zwitterionic compounds. Ten other known in silico prediction methods for aqueous solubility have also been evaluated. It has furthermore been demonstrated that the molecular weight (MW) profile of training sets for the development of aqueous solubility prediction methods can influence their predictive performance with regard to test sets of either matching or diverging profiles. The same property descriptors which have been found most relevant for the prediction of aqueous solubility have also proved useful for the prediction of human plasma protein binding and human volume of distribution at steady-state.

ADME evaluation: 2. A computer model for the prediction of intestinal absorption in humans

Purpose: To develop a computational method to rapidly evaluate human intestinal absorption, one of the drug properties included in the term ADME ( Absorption, Distribution, Metabolism, Excretion). Poor ADME properties are the most important reason for drug failure in clinical development. Methods: The model developed is based on a modified contribution group method in which the basic parameters are structural descriptors identified by the case program, together with the number of hydrogen bond donors. Results: The human intestinal absorption model is a quantitative structure–activity relationship (QSAR) that includes 37 structural descriptors derived from the chemical structures of a data set containing 417 drugs. The model was able to predict the percentage of drug absorbed from the gastrointestinal tract with an r 2 of 0.79 and a standard deviation of 12.32% of the compounds from the training set. The standard deviation for an external test set (50 drugs) was 12.34%. Conclusions: The availability of reliable and fast models like the one we propose here to predict ADME/Tox properties could help speed up the process of finding compounds with improved properties, ultimately making the entire drug discovery process shorter and more cost efficient.

In silico prediction of ADME and pharmacokinetics: Report of an expert meeting organised by COST B15

The computational approach is one of the newest and fastest developing techniques in pharmacokinetics, ADME (absorption, distribution, metabolism, excretion) evaluation, drug discovery and toxicity. However, to date, the software packages devoted to ADME prediction, especially of metabolism, have not yet been adequately validated and still require improvements to be effective. Most are ‘open’ systems, under constant evolution and able to incorporate rapidly, and often easily, new information from user or developer databases. Quantitative in silico predictions are now possible for several pharmacokinetic (PK) parameters, particularly absorption and distribution. The emerging consensus is that the predictions are no worse than those made using in vitro tests, with the decisive advantage that much less investment in technology, resources and time is needed. In addition, and of critical importance, it is possible to screen virtual compounds. Some packages are able to handle thousands of molecules in a few hours. However, common experience shows that, in part at least for essentially irrational reasons, there is currently a lack of confidence in these approaches. An effort should be made by the software producers towards more transparency, in order to improve the confidence of their consumers. It seems highly probable that in silico approaches will evolve rapidly, as did in vitro methods during the last decade. Past experience with the latter should be helpful in avoiding repetition of similar errors and in taking the necessary steps to ensure effective implementation. A general concern is the lack of access to the large amounts of data on compounds no longer in development, but still kept secret by the pharmaceutical industry. Controlled access to these data could be particularly helpful in validating new in silico approaches.

In vitro preclinical lead optimisation technologies (PLOTs) in pharmaceutical development.

The explosion of genuine high throughput technologies has allowed large compound libraries to be screened with ever-increasing biological specificity, exacerbating the problem of lead candidate selection for subsequent drug development. To avoid creating a bottleneck, compounds identified from the high throughput screens undergo lead optimisation by employing medium-throughput screen which permits ranking in terms of their basic absorption, distribution, metabolism, excretion (ADME) and toxicological properties. The historical role of the CRO in the drug discovery/development continuum has been to perform efficacy and toxicology studies, simply to support the regulatory submission of lead candidates. This situation is, however, changing with the development of preclinical lead optimisation technologies facilitating the selection of leading candidates, thereby bridging the gap between high throughput efficacy screens and conventional safety assessment programmes.