Fabio Broccatelli

BDDCS Applied to Over 900 Drugs

Here, we compile the Biopharmaceutics Drug Disposition Classification System (BDDCS) classification for 927 drugs, which include 30 active metabolites. Of the 897 parent drugs, 78.8% (707) are administered orally. Where the lowest measured solubility is found, this value is reported for 72.7% (513) of these orally administered drugs and a dose number is recorded. The measured values are reported for percent excreted unchanged in urine, LogP, and LogD7.4 when available. For all 927 compounds, the in silico parameters for predicted Log solubility in water, calculated LogP, polar surface area, and the number of hydrogen bond acceptors and hydrogen bond donors for the active moiety are also provided, thereby allowing comparison analyses for both in silico and experimentally measured values. We discuss the potential use of BDDCS to estimate the disposition characteristics of novel chemicals (new molecular entities) in the early stages of drug discovery and development. Transporter effects in the intestine and the liver are not clinically relevant for BDDCS class 1 drugs, but potentially can have a high impact for class 2 (efflux in the gut, and efflux and uptake in the liver) and class 3 (uptake and efflux in both gut and liver) drugs. A combination of high dose and low solubility is likely to cause BDDCS class 4 to be underpopulated in terms of approved drugs (N = 53 compared with over 200 each in classes 1–3). The influence of several measured and in silico parameters in the process of BDDCS category assignment is discussed in detail.

A Novel Approach for Predicting P-glycoprotein (ABCB1) Inhibition Using Molecular Interaction Fields

P-glycoprotein (Pgp or ABCB1) is an ABC transporter protein involved in intestinal absorption, drug metabolism, and brain penetration, and its inhibition can seriously alter a drugs bioavailability and safety. In addition, inhibitors of Pgp can be used to overcome multidrug resistance. Given this dual purpose, reliable in silico procedures to predict Pgp inhibition are of great interest. A large and accurate literature collection yielded more than 1200 structures; a model was then constructed using various molecular interaction field-based technologies, considering pharmacophoric features and those physicochemical properties related to membrane partitioning. High accuracy was demonstrated internally with two different validation sets and, moreover, using a number of molecules, for which Pgp inhibition was not experimentally available but was evaluated in-house. All of the validations confirmed the robustness of the model and its suitability to help medicinal chemists in drug discovery. The information derived from the model was rationalized as a pharmacophore for competitive Pgp inhibition.

1,4-Dihydropyridine Scaffold in Medicinal Chemistry, The Story So Far And Perspectives (Part 2): Action in Other Targets and Antitargets

1,4-Dihydropyridines were introduced in the last century for the treatment of coronary diseases. Then medicinal chemists decorated the 1,4-DHP nucleus, the most studied scaffold among L-type calcium channel blockers, achieving diverse activities at several receptors, channels and enzymes. We already described (Ioan et al. Curr. Med. Chem. 2011, 18, 4901-4922) the effects of 1,4-DHPs at ion channels and G-protein coupled receptors. In this paper we continue the analysis of the wide range of biological effects exerted by compounds belonging to this chemical class. In particular, focus is given to the ability of 1,4-DHPs to revert multi drug resistance that, after over 20 years of research, continues to be of great interest. We also describe activities on other targets and the action of 1,4-DHPs against several diseases. Finally, we report and review the interaction of 1,4-DHPs with the hERG channel, transporters and phase I metabolizing enzymes. This work is a starting point for further exploration of the 1,4-DHP core activities on targets, off-targets and antitargets.

1,4-Dihydropyridine Scaffold in Medicinal Chemistry, The Story so Far And Perspectives (Part 1): Action in Ion Channels and GPCRs

Since the pioneering studies of Fleckenstein and co-workers, L-Type Calcium Channel (LTCC) blockers have attracted large interest due to their effectiveness in treating several cardiovascular diseases. Medicinal chemists achieved high potency and tissue selectivity by decorating the 1-4-DHP nucleus, the most studied scaffold among LTCC blockers. Nowadays it is clear that the 1,4-DHP nucleus is a privileged scaffold since, when appropriately substituted, it can selectively modulate diverse receptors, channels and enzymes. Therefore, the 1,4-DHP scaffold could be used to treat various diseases by a single-ligand multi-target approach. In this review, we describe the structure-activity relationships of 1,4-DHPs at ion channels, G-protein coupled receptors, and outline the potential for future therapeutic applications.

BDDCS Class Prediction for New Molecular Entities

The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters, intestinal metabolism, intestinal absorption and plasma protein binding). We further applied the BDDCS prediction model on a large set of medicinal chemistry compounds (over 30,000 chemicals). Based on this application, we suggest that solubility, and not permeability, is the major difference between NMEs and drugs. We anticipate that the forecast of BDDCS categories in early drug discovery may lead to a significant R&D cost reduction.

Improving the prediction of the brain disposition for orally administered drugs using BDDCS

In modeling blood-brain barrier (BBB) passage, in silico models have yielded ~80% prediction accuracy, and are currently used in early drug discovery. Being derived from molecular structural information only, these models do not take into account the biological factors responsible for the in vivo outcome. Passive permeability and P-glycoprotein (Pgp, ABCB1) efflux have been successfully recognized to impact xenobiotic extrusion from the brain, as Pgp is known to play a role in limiting the BBB penetration of oral drugs in humans. However, these two properties alone fail to explain the BBB penetration for a significant number of marketed central nervous system (CNS) agents. The Biopharmaceutics Drug Disposition Classification System (BDDCS) has proved useful in predicting drug disposition in the human body, particularly in the liver and intestine. Here we discuss the value of using BDDCS to improve BBB predictions of oral drugs. BDDCS class membership was integrated with in vitro Pgp efflux and in silico permeability data to create a simple 3-step classification tree that accurately predicted CNS disposition for more than 90% of 153 drugs in our data set. About 98% of BDDCS class 1 drugs were found to markedly distribute throughout the brain; this includes a number of BDDCS class 1 drugs shown to be Pgp substrates. This new perspective provides a further interpretation of how Pgp influences the sedative effects of H1-histamine receptor antagonists.

Transporter-mediated Efflux Influences CNS Side Effects: ABCB1, from Antitarget to Target

We examined the relationship between sedation and orthostatic hypotension, two central side effects and ABCB1 transporter‐mediated efflux for a set of 64 launched drugs that are documented as histamine H1 receptor antagonists. This relationship was placed in the context of passive diffusion (estimated using LogP, the octanol/water partition coefficient), receptor affinity, and the adjusted therapeutic daily dose, in order to account for side effect variability. Within this set, CNS permeability was not dependent on passive diffusion, as no significant differences were found for LogP and its pH‐corrected equivalent, LogD74. Sedation and orthostatic hypotension can be explained within the framework of ABCB1‐mediated efflux and adjusted dose, while target potency has less influence. ABCB1, an antitarget for anticancer agents, acts in fact as a drug target for nonsedating antihistamines. An empirical set of rules, based on the incidence of these two side effects, target affinity and dose was used to predict efflux effects for a number of drugs. Among them, azelastine and mizolastine are predicted to be effluxed via ABCB1‐mediated transport, whereas aripiprazole, clozapine, cyproheptadine, iloperidone, olanzapine, and ziprasidone are likely to be noneffluxed.

QSAR modeling and data mining link Torsades de Pointes risk to the interplay of extent of metabolism, active transport, and HERG liability.

We collected 1173 hERG patch clamp (PC) data (IC50) from the literature to derive twelve classification models for hERG inhibition, covering a large variety of chemical descriptors and classification algorithms. Models were generated using 545 molecules and validated through 258 external molecules tested in PC experiments. We also evaluated the suitability of the best models to predict the activity of 26 proprietary compounds tested in radioligand binding displacement (RBD). Results proved the necessity to use multiple validation sets for a true estimation of model accuracy and demonstrated that using various descriptors and algorithms improves the performance of ligand-based models. Intriguingly, one of the most accurate models uncovered an unexpected link between extent of metabolism and hERG liability. This hypothesis was fairly reinforced by using the Biopharmaceutics Drug Disposition Classification System (BDDCS) that recognized 94% of the hERG inhibitors as extensively metabolized in vivo. Data mining suggested that high Torsades de Pointes (TdP) risk results from an interplay of hERG inhibition, extent of metabolism, active transport, and possibly solubility. Overall, these new findings might improve both the decision making skills of pharmaceutical scientists to mitigate hERG liability during the drug discovery process and the TdP risk assessment during drug development.

Predicting when Biliary Excretion of Parent Drug is a Major Route of Elimination in Humans

Biliary excretion is an important route of elimination for many drugs, yet measuring the extent of biliary elimination is difficult, invasive, and variable. Biliary elimination has been quantified for few drugs with a limited number of subjects, who are often diseased patients. An accurate prediction of which drugs or new molecular entities are significantly eliminated in the bile may predict potential drug-drug interactions, pharmacokinetics, and toxicities. The Biopharmaceutics Drug Disposition Classification System (BDDCS) characterizes significant routes of drug elimination, identifies potential transporter effects, and is useful in understanding drug-drug interactions. Class 1 and 2 drugs are primarily eliminated in humans via metabolism and will not exhibit significant biliary excretion of parent compound. In contrast, class 3 and 4 drugs are primarily excreted unchanged in the urine or bile. Here, we characterize the significant elimination route of 105 orally administered class 3 and 4 drugs. We introduce and validate a novel model, predicting significant biliary elimination using a simple classification scheme. The model is accurate for 83% of 30 drugs collected after model development. The model corroborates the observation that biliarily eliminated drugs have high molecular weights, while demonstrating the necessity of considering route of administration and extent of metabolism when predicting biliary excretion. Interestingly, a predictor of potential metabolism significantly improves predictions of major elimination routes of poorly metabolized drugs. This model successfully predicts the major elimination route for poorly permeable/poorly metabolized drugs and may be applied prior to human dosing.

Ligand Promiscuity between the Efflux Pumps Human P-Glycoprotein and S. aureus NorA.

Thirty-two diverse compounds were evaluated for their ability to inhibit both Pgp-mediated efflux in mouse T-lymphoma L5178 MDR1 and NorA-mediated efflux in S. aureus SA-1199B. Only four compounds were strong inhibitors of both efflux pumps. Three compounds were found to inhibit Pgp exclusively and strongly, while seven compounds inhibited only NorA. These results demonstrate that Pgp and NorA inhibitors do not necessarily overlap, opening the way to safer therapeutic use of effective NorA inhibitors.