Prediction of the permeability of neutral drugs inferred from their solvation properties

Determination of drug absorption is an important component of the drug discovery and development process in that it plays a key role in the decision to promote drug candidates to clinical trials. We have developed a method that, on the basis of an analysis of the dynamic distribution of water molecules around a compound obtained by molecular dynamics simulations, can compute a parameter-free value that correlates very well with the compound permeability measured using the human colon adenocarcinoma (Caco-2) cell line assay

ADME Profiling in Drug Discovery and a New Path Paved on Silica

The drug discovery and development pipeline have more and more relied on in vitro testing and in silico predictions to reduce investments and optimize lead compounds. A comprehensive set of in vitro assays is available to determine key parameters of absorption, distribution, metabolism, and excretion, for example, lipophilicity, solubility, and plasma stability. Such test systems aid the evaluation of the pharmacological properties of a compound and serve as surrogates before entering in vivo testing and clinical trials. Nowadays, computer-aided techniques are employed not just in the discovery of new lead compounds but embedded as part of the entire drug development process where the ADME profiling and big data analyses add a new layer of complexity to those systems. Herein, we give a short overview of the history of the drug development pipeline presenting state-of-the-art ADME in vitro assays as established in academia and industry. We will further introduce the underlying good practices and give an example of the compound development pipeline. In the next step, recent advances at in silico techniques will be highlighted with special emphasis on how pharmacogenomics and in silico PK profiling can enhance drug monitoring and individualization of drug therapy

Application of a Rapid and Integrated Analysis System (RIAS) as a High-Throughput Processing Tool for In Vitro ADME Samples by Liquid Chromatography/Tandem Mass Spectrometry

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Over the past decade, drug discovery programs have started to address the optimization of key ADME properties already at an early stage of the process. Hence, analytical chemists have been confronted with tremendously rising sample numbers and have had to develop methodologies accelerating quantitative liquid chromatography/tandem mass spectrometry (LC/MS/MS). This article focuses on the application of a generic and fully automated LC/MS/MS, named Rapid and Integrated Analysis System (RIAS), as a high-throughput platform for the rapid quantification of drug-like compounds in various in vitro ADME assays. Previous efforts were dedicated to the setup and feasibility study of a workflow-integrated platform combining a modified high-throughput liquid handling LC/MS/MS system controlled by a customized software interface and a customized data-processing and reporting tool. Herein the authors present an extension of this previously developed basic application to a broad set of ADME screening campaigns, covering CYP inhibition, Caco-2, and PAMPA assays. The platform is capable of switching automatically between various ADME assays, performs MS compound optimization if required, and provides a speed of 8 s from sample to sample, independently of the type of ADME assay. Quantification and peak review are adopted to the high-throughput environment and tested against a standard HPLC-ESI technology

Neural network modelling of antifungal activity of a series of oxazole derivatives based on in silico pharmacokinetic parameters

In the present paper, the antifungal activity of a series of benzoxazole and oxazolo[ 4,5-b]pyridine derivatives was evaluated against Candida albicans by using quantitative structure-activity relationships chemometric methodology with artificial neural network (ANN) regression approach. In vitro antifungal activity of the tested compounds was presented by minimum inhibitory concentration expressed as log(1/cMIC). In silico pharmacokinetic parameters related to absorption, distribution, metabolism and excretion (ADME) were calculated for all studied compounds by using PreADMET software. A feedforward back-propagation ANN with gradient descent learning algorithm was applied for modelling of the relationship between ADME descriptors (blood-brain barrier penetration, plasma protein binding, Madin-Darby cell permeability and Caco-2 cell permeability) and experimental log(1/cMIC) values. A 4-6-1 ANN was developed with the optimum momentum and learning rates of 0.3 and 0.05, respectively. An excellent correlation between experimental antifungal activity and values predicted by the ANN was obtained with a correlation coefficient of 0.9536. [Projekat Ministarstva nauke Republike Srbije, br. 172012 i br. 172014

Mass spectrometry and n-in-one analytics in early drug discovery : combinatorial chemistry libraries, lipophilicity and absorption screening

This thesis describes current and past n-in-one methods and presents three early experimental studies using mass spectrometry and the triple quadrupole instrument on the application of n-in-one in drug discovery. N-in-one strategy pools and mix samples in drug discovery prior to measurement or analysis. This allows the most promising compounds to be rapidly identified and then analysed. Nowadays properties of drugs are characterised earlier and in parallel with pharmacological efficacy. Studies presented here use in vitro methods as caco-2 cells and immobilized artificial membrane chromatography for drug absorption and lipophilicity measurements. The high sensitivity and selectivity of liquid chromatography mass spectrometry are especially important for new analytical methods using n-in-one. In the first study, the fragmentation patterns of ten nitrophenoxy benzoate compounds, serial homology, were characterised and the presence of the compounds was determined in a combinatorial library. The influence of one or two nitro substituents and the alkyl chain length of methyl to pentyl on collision-induced fragmentation was studied, and interesting structurefragmentation relationships were detected. Two nitro group compounds increased fragmentation compared to one nitro group, whereas less fragmentation was noted in molecules with a longer alkyl chain. The most abundant product ions were nitrophenoxy ions, which were also tested in the precursor ion screening of the combinatorial library. In the second study, the immobilized artificial membrane chromatographic method was transferred from ultraviolet detection to mass spectrometric analysis and a new method was developed. Mass spectra were scanned and the chromatographic retention of compounds was analysed using extract ion chromatograms. When changing detectors and buffers and including n-in-one in the method, the results showed good correlation. Finally, the results demonstrated that mass spectrometric detection with gradient elution can provide a rapid and convenient n-in-one method for ranking the lipophilic properties of several structurally diverse compounds simultaneously. In the final study, a new method was developed for caco-2 samples. Compounds were separated by liquid chromatography and quantified by selected reaction monitoring using mass spectrometry. This method was used for caco-2 samples, where absorption of ten chemically and physiologically different compounds was screened using both single and nin- one approaches. These three studies used mass spectrometry for compound identification, method transfer and quantitation in the area of mixture analysis. Different mass spectrometric scanning modes for the triple quadrupole instrument were used in each method. Early drug discovery with n-in-one is area where mass spectrometric analysis, its possibilities and proper use, is especially important.Tämä tutkielma kertoo vanhoista sekä uusista seosanalytiikan muodoista lääkkeen keksimisessä ja kehittämisessä sekä esittelee kolme varhaista kokeellista tutkimusta käyttäen kolmoiskvadrupoli massa spektrometri -laitteistoa. Lääkekehityksen seosanalytiikka, n-in-one, yhdistää tutkittavia kemikaaleja ja niiden näytteitä sekä ennen kokeellista määritystä että analysointia, ja siten mahdollistaa uusien yhdisteiden löytämisen ja tunnistamisen nopeasti. Aikaisessa vaiheessa, samanaikaisesti farmakologisten vaikutusten seulonnan kanssa, määritetään myös yhdisteiden ominaisuuksia. Työssä esitetyt in vitro menetelmät, kuten caco-2 solut ja kromatografiset lipidikalvot, mallintavat lääkeaineiden imeytymistä ja lipofiilisyyttä. Nestekromatografia massa spektrometria on herkkä ja erittäin erottelukykyinen tekniikka ja siksi se on erityisen tärkeä uusissa seosanalytiikan menetelmissä. Ensimmäisessä työssä tutkittiin kymmenen nitrofenoksibentsoaatti -rakenteisen molekyylin massaspektrometrinen hajoaminen. Tämä homologisarja myös yhdistettiin ja määritettiin kuin kombinatorinen kirjasto. Nitro ja di-nitro substituointi sekä molekyylien eripituiset alkyyliketjut aiheuttivat massaspektrometrisesti mielenkiintoisia rakenne/hajoaminen -suhteita. Kaksi nitro-ryhmää lisäsi hajoamista verrattuna yhteen, kun taas pidemmillä alkyyliketjuilla oli rakennetta stabiloiva vaikutus. Nitrofenoksi-ioni oli vallitsevin hajoamistuote, ja tätä hyödynnettiin myös kombinatorisen kirjaston seulonnassa. Seuraavassa työssä kehitettiin uusi menetelmä kromatografiselle lipidikalvolle, IAM – kolonnille, jossa vanhan menetelmän ultravioletti detektori vaihdettiin massa spektrometriksi. Yhdisteiden retentioajat määritettiin skannaamalla massaspektrejä ja suodattamalla spektreistä ionikromatogrammit. Tulokset osoittivat hyvää korrelaatiota vanhan menetelmän tuloksiin, vaikka menetelmässä vaihdettiin detektoria, puskuria ja lisättiin mahdollisuus seosanalytiikkaan. Lisäksi työssä esiteltiin nopea n-in-one menetelmä erilaisten yhdisteiden samanaikaiseen lipofiilisyyden luokitteluun käyttäen massaspektrometriä sekä gradienttia. Viimeisessä tutkimuksessa kehitettiin uusi menetelmä caco-2 näytteille. Yhdisteet erotettiin nestekromatografisesti ja pitoisuudet määritettiin massaspektrometrisesti valittujen reaktioiden avulla. Tätä menetelmää käytettiin caco-2 näytteille, jossa kymmenen kemiallisesti ja fysiologisesti erilaisen yhdisteen imeytyminen tutkittiin yksittäin sekä käyttäen seosanalyysejä. Nämä kolme tutkimusta käyttivät massaspektrometriaa ja seosanalytiikkaa yhdisteiden tunnistamisessa, menetelmän siirrossa sekä pitoisuuden määrityksessä. Jokaisessa tutkimuksessa käytettiin kolmoiskvadrupoli massaspektrometrin eri määritystapoja. Massaspektrometrisen analytiikan mahdollisuudet ja oikeanlainen käyttö ovat erityisen tärkeitä kun määritetään seoksia lääkkeen keksimisen varhaisessa vaiheessa

Towards human-relevant preclinical models: fluid-dynamics and three-dimensionality as key elements

The activity of research of this thesis focuses on the relevance that appropriate in vitro fully humanized models replicating physiological microenvironments and cues (e.g., mechanical and fluidic) are essential for improving human biology knowledge and boosting new compound testing. In biomedical research, the high percentage of the low rate of successful translation from bench to bedside failure is often attributed to the inability of preclinical models in generating reliable results. Indeed, it is well known that 2D models are far from being representative of human complexity and, on the other side, although animal tests are currently required by regulatory organizations, they are commonly considered unpredictive. As a matter of fact, there is a growing awareness that 3D human tissue models and fluid-dynamic scenarios are better reproducers of the in vivo context. Therefore, during this PhD, I have worked to model and validate technologically advanced fluidic platforms, where to replicate biological processes in a systemic and dynamic environment to better assess the pharmacokinetics and the pharmacodynamics of drug candidates, by considering different case studies. First, skin absorption assays have been performed accordingly to the OECD Test Guidelines 428 comparing the standard diffusive chamber (Franz Diffusion Cell) to a novel fluidic commercially available organ on chip platform (MIVO), demonstrating the importance of emulating physiological fluid flows beneath the skin to obtain in vivo-like transdermal penetration kinetics. On the other hand, after an extensive research analysis of the currently available intestinal models, which resulted insufficient in reproducing chemicals and food absorption profiles in vivo, a mathematical model of the intestinal epithelium as a novel screening strategy has been developed. Moreover, since less than 8% of new anticancer drugs are successfully translated from preclinical to clinical trials, breast, and ovarian cancer, which are among the 5 most common causes of death in women, and neuroblastoma, which has one of the lowest survival rates of all pediatric cancers, have been considered. For each, I developed and optimized 3D ECM-like tumor models, then cultured them under fluid-dynamic conditions (previously predicted by CFD simulations) by adopting different (customized or commercially available) fluidic platforms that allowed to mimic u stimuli (fluid velocity and the fluid flow-induced shear stress) and investigate their impact on tumor cells viability and drug response. I provided evidence that such an approach is pivotal to clinically reproduce the complexity and dynamics of the cancer phenomenon (onset, progression, and metastasis) as well as to develop and validate traditional (i.e., platin-based drugs, caffein active molecule) or novel treatment strategies (i.e., hydroxyapatite nanoparticles, NK cells-based immunotherapies)

Advances in the Prediction of Gastrointestinal Absorption: Quantitative Structure-Activity Relationship (QSAR) modelling of PAMPA Permeability

Gastrointestinal absorption (GI absorption) is a key absorption, distribution, metabolism, and excretion (ADME) property when the biological effects of substances are evaluated. The Parallel Artificial Membrane Permeability Assay (PAMPA) has emerged as a primary screen for determining passive transcellular permeability, the dominant GI absorption mechanism for many drugs, thus helping with the prioritisation of the most promising lead compounds for pharmacokinetic studies. Recently the PAMPA assay has attracted increasing interest from various other industrial sectors, including cosmetics, where such non-animal models may provide a crucial source of information for in vitro - in vivo extrapolation. This method is also a reliable source of experimental data for Quantitative Structure-Activity Relationship (QSAR) modelling of GI absorption. In this investigation, published QSAR models for PAMPA were reviewed with the aim to summarise and assess critically the current state of the art. The review indicates a relatively small number of QSARs compared to some endpoints, but much consistency within the models. PAMPA permeability increases with hydrophobicity and decreases with the surface area occupied by hydrogen bond acceptor/donor atoms. The models can be applied to screening for bioactive compounds with the potential to pass the gastrointestinal barrier as well as to design new structures with increased PAMPA permeability, thus with better expectations towards improved in vivo GI absorption

SYNTHESIS, IN SILICO CHARACTERIZATION AND EX VIVO EVALUATION OF THE NOVEL ORGANIC NITRATE NDIBP AS A POTENTIAL VASORELAXANT AGENT

Objective: This study aimed to describe the synthesis and biological/pharmacokinetic potential of the 1,3-diisobutoxypropan-2-yl nitrate (NDIBP) using in silico and ex vivo approaches. Methods: The compound was characterized by Fourier-transform infrared spectroscopy and 1H and 13C- nuclear magnetic resonance spectra. NDIBP biological activity spectrum was obtained by Prediction of Activity Spectra for Substances (PASS). The pharmacological effect was validated in ex vivo studies using mesenteric artery. Drug-like properties and Absorption Distribution Metabolism Excretion and Toxicity (ADMET) studies were carried out by pkCSM (Predicting Small-Molecule Pharmacokinetic Properties Using Graph-Based Signatures) software. Results: PASS prediction indicated NDIBP as nitric oxide (NO) donor with vasodilator effect. Ex vivo studies validated PASS analysis and showed the NDIBP vasorelaxant activity in mesenteric arteries. Physicochemical parameters and ADMET prediction suggested that NDIBP is a drug-like molecule with a good theoretical oral bioavailability, good absorption in the gastrointestinal tract, and a low distribution in the tissues. Conclusion: All the data indicated that NDIBP possesses biological activities and drug-like properties to be considered as a vasorelaxant agent and a good candidate for further investigation in the treatment of arterial hypertension and drug development studies