Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions.

Targeting protein-protein interactions (PPIs) by small molecule modulators (iPPIs) is an attractive strategy for drug therapy, and some iPPIs have already been introduced into the clinic. Blocking PPIs is however considered to be a more difficult task than inhibiting enzymes or antagonizing receptor activity. In this paper, we examine whether it is possible to predict the likelihood of molecules to act as iPPIs. Using our in-house iterative stochastic elimination (ISE) algorithm, we constructed two classification models that successfully distinguish between iPPIs from the iPPI-DB database and decoy molecules from either the Enamine HTS collection (ISE 1) or the ZINC database (ISE 2). External test sets of iPPIs taken from the TIMBAL database and decoys from Enamine HTS or ZINC were screened by the models: the area under the curve for the receiver operating characteristic curve was 0.85-0.89, and the Enrichment Factor increased from an initial 1 to as much as 66 for ISE 1 and 57 for ISE 2. Screening of the Enamine HTS and ZINC data sets through both models results in a library of ∼1.3 million molecules that pass either one of the models. This library is enriched with iPPI candidates that are structurally different from known iPPIs, and thus, it is useful for target-specific screenings and should accelerate the discovery of iPPI drug candidates. The entire library is available in Table S6.

In silico Docking Analysis for Blocking JUNO-IZUMO1 Interaction Identifies Two Small Molecules that Block in vitro Fertilization.

Combined hormone drugs are the basis for orally administered contraception. However, they are associated with severe side effects that are even more impactful for women in developing countries, where resources are limited. The risk of side effects may be reduced by non-hormonal small molecules which specifically target proteins involved in fertilization. In this study, we present a virtual docking experiment directed to discover molecules that target the crucial fertilization interactions of JUNO (oocyte) and IZUMO1 (sperm). We docked 913,000 molecules to two crystal structures of JUNO and ranked them on the basis of energy-related criteria. Of the 32 tested candidates, two molecules (i.e., Z786028994 and Z1290281203) demonstrated fertilization inhibitory effect in both an in vitro fertilization (IVF) assay in mice and an in vitro penetration of human sperm into hamster oocytes. Despite this clear effect on fertilization, these two molecules did not show JUNO-IZUMO1 interaction blocking activity as assessed by AVidity-based EXtracellular Interaction Screening (AVEXIS). Therefore, further research is required to determine the mechanism of action of these two fertilization inhibitors.

Segmental-Dependent Intestinal Drug Permeability: Development and Model Validation of In Silico Predictions Guided by In Vivo Permeability Values.

The goal of this work was to develop an in silico model that allows predicting segmental-dependent permeability throughout the small intestine (SI). In vivo permeability of 11 model drugs in 3 SI segments (jejunum, mid-SI, ileum) was studied in rats, creating a data set that reflects the conditions throughout the SI. Then, a predictive model was developed, combining physicochemical drug properties influencing the underlying mechanism of passive permeability: Log p, polar surface area, MW, H-bond count, and Log fu, with microenvironmental SI conditions. Excellent correlation was evident between the predicted and experimental data (R2 = 0.914), with similar predictability in each SI segment. Log p and Log fu were identified as the major determinants of permeability, with similar contribution. Total H-bond count was also a significant determinant, followed by polar surface area and MW. Leaving out any of the model parameters decreased its predictability. The model was validated against 5 external drugs, with excellent predictability. Notably, the model was able to predict the segmental-dependent permeability of all drugs showing this trend experimentally. Model predictability was better in the high-permeability versus low-permeability range. Overall, our approach of constructing a straightforward in silico model allowed reliable predictions of segmental-dependent intestinal permeability, providing new insights into relative effects of drug-related factors and gastrointestinal environment on permeability.

Oral drug therapy following bariatric surgery: an overview of fundamentals, literature and clinical recommendations.

Bariatric surgery is the most effective solution for severe obesity and obesity with comorbidities, and the number of patients going through bariatric surgery is rapidly and constantly growing. The modified gastrointestinal anatomy of the patient may lead to significant pharmacokinetic alterations in the oral absorption of drugs after the surgery; however, because of insufficient available literature and inadequate awareness of the medical team, bariatric surgery patients may be discharged from the hospital with insufficient instructions regarding their medication therapy. In this article, we aim to present the various mechanisms by which bariatric surgery may influence oral drug absorption, to provide an overview of the currently available literature on the subject, and to draw guidelines for the recommendations bariatric surgery patients should be instructed before leaving the hospital. To date, and until more robust data are published, it is essential to follow and monitor patients closely for safety and efficacy of their medication therapies, both in the immediate and distant time post-surgery.

In-situ intestinal rat perfusions for human Fabs prediction and BCS permeability class determination: Investigation of the single-pass vs. the Doluisio experimental approaches.

Intestinal drug permeability has been recognized as a critical determinant of the fraction dose absorbed, with direct influence on bioavailability, bioequivalence and biowaiver. The purpose of this research was to compare intestinal permeability values obtained by two different intestinal rat perfusion methods: the single-pass intestinal perfusion (SPIP) model and the Doluisio (closed-loop) rat perfusion method. A list of 15 model drugs with different permeability characteristics (low, moderate, and high, as well as passively and actively absorbed) was constructed. We assessed the rat intestinal permeability of these 15 model drugs in both SPIP and the Doluisio methods, and evaluated the correlation between them. We then evaluated the ability of each of these methods to predict the fraction dose absorbed (Fabs) in humans, and to assign the correct BCS permeability class membership. Excellent correlation was obtained between the two experimental methods (r(2)=0.93). An excellent correlation was also shown between literature Fabs values and the predictions made by both rat perfusion techniques. Similar BCS permeability class membership was designated by literature data and by both SPIP and Doluisio methods for all compounds. In conclusion, the SPIP model and the Doluisio (closed-loop) rat perfusion method are both equally useful for obtaining intestinal permeability values that can be used for Fabs prediction and BCS classification.

Dipeptidyl peptidase IV as a potential target for selective prodrug activation and chemotherapeutic action in cancers.

The efficacy of chemotherapeutic drugs is often offset by severe side effects attributable to poor selectivity and toxicity to normal cells. Recently, the enzyme dipeptidyl peptidase IV (DPPIV) was considered as a potential target for the delivery of chemotherapeutic drugs. The purpose of this study was to investigate the feasibility of targeting chemotherapeutic drugs to DPPIV as a strategy to enhance their specificity. The expression profile of DPPIV was obtained for seven cancer cell lines using DNA microarray data from the DTP database, and was validated by RT-PCR. A prodrug was then synthesized by linking the cytotoxic drug melphalan to a proline-glycine dipeptide moiety, followed by hydrolysis studies in the seven cell lines with a standard substrate, as well as the glycyl-prolyl-melphalan (GP-Mel). Lastly, cell proliferation studies were carried out to demonstrate enzyme-dependent activation of the candidate prodrug. The relative RT-PCR expression levels of DPPIV in the cancer cell lines exhibited linear correlation with U95Av2 Affymetrix data (r(2) = 0.94), and with specific activity of a standard substrate, glycine-proline-p-nitroanilide (r(2) = 0.96). The significantly higher antiproliferative activity of GP-Mel in Caco-2 cells (GI50 = 261 µM) compared to that in SK-MEL-5 cells (GI50 = 807 µM) was consistent with the 9-fold higher specific activity of the prodrug in Caco-2 cells (5.14 pmol/min/µg protein) compared to SK-MEL-5 cells (0.68 pmol/min/µg protein) and with DPPIV expression levels in these cells. Our results demonstrate the great potential to exploit DPPIV as a prodrug activating enzyme for efficient chemotherapeutic drug targeting.

Provisional in-silico biopharmaceutics classification (BCS) to guide oral drug product development.

The main objective of this work was to investigate in-silico predictions of physicochemical properties, in order to guide oral drug development by provisional biopharmaceutics classification system (BCS). Four in-silico methods were used to estimate LogP: group contribution (CLogP) using two different software programs, atom contribution (ALogP), and element contribution (KLogP). The correlations (r(2)) of CLogP, ALogP and KLogP versus measured LogP data were 0.97, 0.82, and 0.71, respectively. The classification of drugs with reported intestinal permeability in humans was correct for 64.3%-72.4% of the 29 drugs on the dataset, and for 81.82%-90.91% of the 22 drugs that are passively absorbed using the different in-silico algorithms. Similar permeability classification was obtained with the various in-silico methods. The in-silico calculations, along with experimental melting points, were then incorporated into a thermodynamic equation for solubility estimations that largely matched the reference solubility values. It was revealed that the effect of melting point on the solubility is minor compared to the partition coefficient, and an average melting point (162.7 °C) could replace the experimental values, with similar results. The in-silico methods classified 20.76% (± 3.07%) as Class 1, 41.51% (± 3.32%) as Class 2, 30.49% (± 4.47%) as Class 3, and 6.27% (± 4.39%) as Class 4. In conclusion, in-silico methods can be used for BCS classification of drugs in early development, from merely their molecular formula and without foreknowledge of their chemical structure, which will allow for the improved selection, engineering, and developability of candidates. These in-silico methods could enhance success rates, reduce costs, and accelerate oral drug products development.

Biowaiver monographs for immediate-release solid oral dosage forms: codeine phosphate.

The present monograph reviews data relevant to applying the biowaiver procedure for the approval of immediate-release multisource solid dosage forms containing codeine phosphate. Both biopharmaceutical and clinical data of codeine were assessed. Solubility studies revealed that codeine meets the "highly soluble" criteria according to World Health Organization (WHO), the European Medicines Agency (EMA), and the United States Food and Drug Administration (US FDA). Codeine's fraction of dose absorbed in humans was reported to be high (>90%) based on cumulative urinary excretion of drug and drug-related material following oral administration. The permeability of codeine was also assessed to be high in both Caco-2 monolayers and rat intestinal perfusion studies. The main risks associated with codeine, that is, toxicity (attributed to CYP2D6 polymorphism) and its abuse potential, are present irrespective of the dosage form, and do not need to be taken into account for bioequivalence (BE) considerations. Taken together, codeine is a class 1 drug with manageable risk and is a good candidate for waiver of in vivo BE studies.

The low/high BCS permeability class boundary: physicochemical comparison of metoprolol and labetalol.

Although recognized as overly conservative, metoprolol is currently the common low/high BCS permeability class boundary reference compound, while labetalol was suggested as a potential alternative. The purpose of this study was to identify the various characteristics that the optimal marker should exhibit, and to investigate the suitability of labetalol as the permeability class reference drug. Labetalol's BCS solubility class was determined, and its physicochemical properties and intestinal permeability were thoroughly investigated, both in vitro and in vivo in rats, considering the complexity of the whole of the small intestine. Labetalol was found to be unequivocally a high-solubility compound. In the pH range throughout the small intestine (6.5-7.5), labetalol exhibited pH-dependent permeability, with higher permeability at higher pH values. While in vitro octanol-buffer partitioning (Log D) values of labetalol were significantly higher than those of metoprolol, the opposite was evident in the in vitro PAMPA permeability assay. The results of the in vivo perfusion studies in rats lay between the two contradictory in vitro studies; metoprolol was shown to have moderately higher rat intestinal permeability than labetalol. Theoretical distribution of the ionic species of the drugs was in corroboration with the experimental in vitro and the in vivo data. We propose three characteristics that the optimal permeability class reference drug should exhibit: (1) fraction dose absorbed in the range of 90%; (2) the optimal marker drug should be absorbed largely via passive transcellular permeability, with no/negligible carrier-mediated active intestinal transport (influx or efflux); and (3) the optimal marker drug should preferably be nonionizable. The data presented in this paper demonstrate that neither metoprolol nor labetalol can be regarded as optimal low/high-permeability class boundary standard. While metoprolol is too conservative due to its complete absorption, labetalol has been shown to be a substrate for P-gp-mediated efflux transport, and both drugs exhibit significant segmental-dependent permeability along the gastrointestinal tract. Nevertheless, the use of metoprolol as the marker compound does not carry a risk of bioinequivalence: Peff value similar to or higher than metoprolol safely indicates high-permeability classification. On the other hand, a more careful data analysis is needed if labetalol is used as the reference compound.

Purely in silico BCS classification: science based quality standards for the world's drugs.

BCS classification is a vital tool in the development of both generic and innovative drug products. The purpose of this work was to provisionally classify the world's top selling oral drugs according to the BCS, using in silico methods. Three different in silico methods were examined: the well-established group contribution (CLogP) and atom contribution (ALogP) methods, and a new method based solely on the molecular formula and element contribution (KLogP). Metoprolol was used as the benchmark for the low/high permeability class boundary. Solubility was estimated in silico using a thermodynamic equation that relies on the partition coefficient and melting point. The validity of each method was affirmed by comparison to reference data and literature. We then used each method to provisionally classify the orally administered, IR drug products found in the WHO Model list of Essential Medicines, and the top-selling oral drug products in the United States (US), Great Britain (GB), Spain (ES), Israel (IL), Japan (JP), and South Korea (KR). A combined list of 363 drugs was compiled from the various lists, and 257 drugs were classified using the different in silico permeability methods and literature solubility data, as well as BDDCS classification. Lastly, we calculated the solubility values for 185 drugs from the combined set using in silico approach. Permeability classification with the different in silico methods was correct for 69-72.4% of the 29 reference drugs with known human jejunal permeability, and for 84.6-92.9% of the 14 FDA reference drugs in the set. The correlations (r(2)) between experimental log P values of 154 drugs and their CLogP, ALogP and KLogP were 0.97, 0.82 and 0.71, respectively. The different in silico permeability methods produced comparable results: 30-34% of the US, GB, ES and IL top selling drugs were class 1, 27-36.4% were class 2, 22-25.5% were class 3, and 5.46-14% were class 4 drugs, while ∼8% could not be classified. The WHO list included significantly less class 1 and more class 3 drugs in comparison to the countries' lists, probably due to differences in commonly used drugs in developing vs industrial countries. BDDCS classified more drugs as class 1 compared to in silico BCS, likely due to the more lax benchmark for metabolism (70%), in comparison to the strict permeability benchmark (metoprolol). For 185 out of the 363 drugs, in silico solubility values were calculated, and successfully matched the literature solubility data. In conclusion, relatively simple in silico methods can be used to estimate both permeability and solubility. While CLogP produced the best correlation to experimental values, even KLogP, the most simplified in silico method that is based on molecular formula with no knowledge of molecular structure, produced comparable BCS classification to the sophisticated methods. This KLogP, when combined with a mean melting point and estimated dose, can be used to provisionally classify potential drugs from just molecular formula, even before synthesis. 49-59% of the world's top-selling drugs are highly soluble (class 1 and class 3), and are therefore candidates for waivers of in vivo bioequivalence studies. For these drugs, the replacement of expensive human studies with affordable in vitro dissolution tests would ensure their bioequivalence, and encourage the development and availability of generic drug products in both industrial and developing countries.

New targeting strategies in drug therapy of inflammatory bowel disease: mechanistic approaches and opportunities.

INTRODUCTION: Inflammatory bowel disease (IBD) is an exceptional scenario with regard to drug targeting, as oral administration has the potential to deliver the drug directly to the site(s) of action. Consequently, retention of the drug within the intestinal lumen and tissue, rather than systemic absorption, is frequently desirable. AREAS COVERED: In this article, the traditional drug-delivery strategies used in IBD are briefly summarized. These include rectal dosage forms and oral systems that target the lower intestine/colon by pH-, time-, microflora-, and pressure-dependent mechanisms. Then, the article offers an updated overview of recently developed delivery systems aimed to achieve maximal drug concentrations in the inflamed intestinal tissues with minimal systemic side effects. These include antibodies, small molecules, Janus kinase inhibitors, particulate carrier systems, anti-inflammatory peptides, gene therapy, and transgenic bacteria. The various approaches are reviewed, and the challenges that still remain to be overcome are discussed. EXPERT OPINION: The molecular revolution of the past decade profoundly influenced the treatment and management of IBD. In the coming years, this trend is expected to continue. Yet, many challenges are still ahead. A strong collaborative effort by experts from different fields is encouraged and necessary to maximize our success in IBD drug targeting.