Literature Review: Investigating Drug Transport at the Blood-Brain Barrier and the Effects of P-glycoprotein on the Brain Distribution of Drugs.

The blood-brain barrier (BBB) is a unique barrier that strictly regulates the entry of endogenous substrates and xenobiotics into the brain. This is due to its tight junctions and the array of transporters and metabolic enzymes that are expressed. The determination of brain concentrations in vivo is difficult, laborious and expensive which means that there is interest in developing predictive tools of brain distribution. Predicting brain concentrations is important even in early drug development to ensure efficacy of central nervous system (CNS) targeted drugs and safety of non-CNS drugs. The literature review covers the most common current in vitro, in vivo and in silico methods of studying transport into the brain, concentrating on transporter effects. The consequences of efflux mediated by p-glycoprotein, the most widely characterized transporter expressed at the BBB, is also discussed. The aim of the experimental study was to build a pharmacokinetic (PK) model to describe p-glycoprotein substrate drug concentrations in the brain using commonly measured in vivo parameters of brain distribution. The possibility of replacing in vivo parameter values with their in vitro counterparts was also studied. All data for the study was taken from the literature. A simple 2-compartment PK model was built using the Stella™ software. Brain concentrations of morphine, loperamide and quinidine were simulated and compared with published studies. Correlation of in vitro measured efflux ratio (ER) from different studies was evaluated in addition to studying correlation between in vitro and in vivo measured ER. A Stella™ model was also constructed to simulate an in vitro transcellular monolayer experiment, to study the sensitivity of measured ER to changes in passive permeability and Michaelis-Menten kinetic parameter values. Interspecies differences in rats and mice were investigated with regards to brain permeability and drug binding in brain tissue. Although the PK brain model was able to capture the concentration-time profiles for all 3 compounds in both brain and plasma and performed fairly well for morphine, for quinidine it underestimated and for loperamide it overestimated brain concentrations. Because the ratio of concentrations in brain and blood is dependent on the ER, it is suggested that the variable values cited for this parameter and its inaccuracy could be one explanation for the failure of predictions. Validation of the model with more compounds is needed to draw further conclusions. In vitro ER showed variable correlation between studies, indicating variability due to experimental factors such as test concentration, but overall differences were small. Good correlation between in vitro and in vivo ER at low concentrations supports the possibility of using of in vitro ER in the PK model. The in vitro simulation illustrated that in the simulation setting, efflux is significant only with low passive permeability, which highlights the fact that the cell model used to measure ER must have low enough paracellular permeability to correctly mimic the in vivo situation.

Understanding PK/PD

Pharmacology is the science underpinning dosing, mechanisms of action and effectiveness of drugs. Central to pharmacology, are the studies of pharmacokinetics (PK) and pharmacodynamics (PD). On one hand, PK defines the time-course of drug concentrations in the body and incorporates the broad concepts of drug absorption, distribution, metabolism and elimination. On the other hand, PD describes the relationship between drug concentrations and pharmacological effects. In practice, PK is often referred as “what the body does to the drug” whilst PD as “what the drug does to the body”. Thus, PK/PD describes the relationship between drug dose and pharmacological effects with changes in drug concentrations leading to different pharmacological effects.

Veri-aivoesteen farmakokineettinen malli permeaation ja efluksin merkityksen tutkimiseen

Veri-aivoeste suojelee aivoja verenkierron vierasaineilta. Veri-aivoestettä tutkivia in vivo ja in vitro -menetelmiä on raportoitu laajasti kirjallisuudessa. Yhdisteiden farmakokinetiikka aivoissa kuvaavia tietokonemalleja on esitetty vain muutamia. Tässä tutkimuksessa kerättiin kirjallisuudesta aineisto eri in vitro ja in vivo -menetelmillä määritetyistä veri-aivoesteen permeabiliteettikertoimista. Lisäksi tutkimuksessa rakennettiin kaksi veri-aivoesteen farmakokineettista tietokonemallia, mikrodialyysimalli ja efluksimalli. Mikrodialyysimalli on yksinkertainen kahdesta tilasta (verenkierto ja aivot) koostuva farmakokineettinen malli. Mikrodialyysimallilla simuloitiin in vivo määritettyjen parametrien perusteella viiden yhdisteen pitoisuuksia rotan aivoissa ja verenkierrossa. Mallilla ei saatu täsmällisesti in vivo -tilannetta vastaavia pitoisuuskuvaajia johtuen mallin rakenteessa tehdyistä yksinkertaistuksista, kuten aivokudostilan ja kuljetinproteiinien kinetiikan puuttuminen. Efluksimallissa on kolme tilaa, verenkierto, veri-aivoesteen endoteelisolutila ja aivot. Efluksimallilla tutkittiin teoreettisten simulaatioiden avulla veri-aivoesteen luminaalisella membraanilla sijaitsevan aktiivisen efluksiproteiinin ja passiivisen permeaation merkitystä yhdisteen pitoisuuksiin aivojen solunulkoisessa nesteessä. Tutkittava parametri oli vapaan yhdisteen pitoisuuksien suhde aivojen ja verenkierron välillä vakaassa tilassa (Kp,uu). Tuloksissa havaittiin efluksiproteiinin vaikutus pitoisuuksiin Michaelis-Mentenin kinetiikan mukaisesti. Efluksimalli sopii hyvin teoreettisten simulaatioiden tekemiseen. Malliin voidaan lisätä aktiivisia kuljettimia. Teoreettisten simulaatioiden avulla voidaan yhdistää in vitro ja in vivo tutkimuksien tuloksia ja osatekijöitä voidaan tutkia yhdessä simulaatiossa.

Understanding Aqueous Dispersibility of Graphene Oxide and Reduced Graphene Oxide through pK(a) Measurements

The chemistry underlying the aqueous dispersibility of graphene oxide (GO) and reduced graphene oxide (r-GO) is a key consideration in the design of solution processing techniques for the preparation of processable graphene sheets. Here, we use zeta potential measurements, pH titrations, and infrared spectroscopy to establish the chemistry underlying the aqueous dispersibility of GO and r-GO sheets at different values of pH. We show that r-GO sheets have ionizable groups with a single pK value (8.0) while GO sheets have groups that are more acidic (pK = 4.3), in addition to groups with pK values of 6.6 and 9.0. Infrared spectroscopy has been used to follow the sequence of ionization events. In both GO and r-GO sheets, it is ionization of the carboxylic groups that is primarily responsible for the build up of charge, but on GO sheets, the presence of phenolic and hydroxyl groups in close proximity to the carboxylic groups lowers the pK(a) value by stabilizing the carboxylate anion, resulting in superior water dispersibility.

Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pK(a) of ionizable residues in proteins

Residue depth accurately measures burial and parameterizes local protein environment. Depth is the distance of any atom/residue to the closest bulk water. We consider the non-bulk waters to occupy cavities, whose volumes are determined using a Voronoi procedure. Our estimation of cavity sizes is statistically superior to estimates made by CASTp and VOIDOO, and on par with McVol over a data set of 40 cavities. Our calculated cavity volumes correlated best with the experimentally determined destabilization of 34 mutants from five proteins. Some of the cavities identified are capable of binding small molecule ligands. In this study, we have enhanced our depth-based predictions of binding sites by including evolutionary information. We have demonstrated that on a database (LigASite) of similar to 200 proteins, we perform on par with ConCavity and better than MetaPocket 2.0. Our predictions, while less sensitive, are more specific and precise. Finally, we use depth (and other features) to predict pK(a)s of GLU, ASP, LYS and HIS residues. Our results produce an average error of just <1 pH unit over 60 predictions. Our simple empirical method is statistically on par with two and superior to three other methods while inferior to only one. The DEPTH server (http://mspc.bii.a-star.edu.sg/depth/) is an ideal tool for rapid yet accurate structural analyses of protein structures.

Dissociation Constants of Weak Acids from ab Initio Molecular Dynamics Using Metadynamics: Influence of the Inductive Effect and Hydrogen Bonding on pK(a) Values

The theoretical estimation of the dissociation constant, or pK(a), of weak acids continues to be a challenging field. Here, we show that ab initio CarParrinello molecular dynamics simulations in conjunction with metadynamics calculations of the free-energy profile of the dissociation reaction provide reasonable estimates of the pK(a) value. Water molecules, sufficient to complete the three hydration shells surrounding the acid molecule, were included explicitly in the computation procedure. The free-energy profiles exhibit two distinct minima corresponding to the dissociated and neutral states of the acid, and the difference in their values provides the estimate for pK(a). We show for a series of organic acids that CPMD simulations in conjunction with metadynamics can provide reasonable estimates of pK(a) values. The acids investigated were aliphatic carboxylic acids, chlorine-substituted carboxylic acids, cis- and trans-butenedioic acid, and the isomers of hydroxybenzoic acid. These systems were chosen to highlight that the procedure could correctly account for the influence of the inductive effect as well as hydrogen bonding on pK(a) values of weak organic acids. In both situations, the CPMD metadynamics procedure faithfully reproduces the experimentally observed trend and the magnitudes of the pK(a) values.

Estimating successive pK(a) values of polyprotic acids from ab initio molecular dynamics using metadynamics: the dissociation of phthalic acid and its isomers

Estimation of the dissociation constant, or pK(a), of weak acids continues to be a central goal in theoretical chemistry. Here we show that ab initio Car-Parrinello molecular dynamics simulations in conjunction with metadynamics calculations of the free energy profile of the dissociation reaction can provide reasonable estimates of the successive pK(a) values of polyprotic acids. We use the distance-dependent coordination number of the protons bound to the hydroxyl oxygen of the carboxylic group as the collective variable to explore the free energy profile of the dissociation process. Water molecules, sufficient to complete three hydration shells surrounding the acid molecule, were included explicitly in the computation procedure. Two distinct minima corresponding to the dissociated and un-dissociated states of the acid are observed and the difference in their free energy values provides the estimate for pK(a), the acid dissociation constant. We show that the method predicts the pK(a) value of benzoic acid in good agreement with experiment and then show using phthalic acid (benzene dicarboxylic acid) as a test system that both the first and second pK(a) values as well, as the subtle difference in their values for different isomers can be predicted in reasonable agreement with experimental data.

Ab Initio Molecular Dynamics Simulations of Amino Acids in Aqueous Solutions: Estimating pK(a) Values from Metadynamics Sampling

Changes in the protonation and deprotonation of amino acid residues in proteins play a key role in many biological processes and pathways. Here, we report calculations of the free-energy profile for the protonation deprotonation reaction of the 20 canonical alpha amino acids in aqueous solutions using ab initio Car-Parrinello molecular dynamics simulations coupled with metad-ynamics sampling. We show here that the calculated change in free energy of the dissociation reaction provides estimates of the multiple pK(a) values of the amino acids that are in good agreement with experiment. We use the bond-length-dependent number of the protons coordinated to the hydroxyl oxygen of the carboxylic and the amine groups as the collective variables to explore the free-energy profiles of the Bronsted acid-base chemistry of amino acids in aqueous solutions. We ensure that the amino acid undergoing dissociation is solvated by at least three hydrations shells with all water molecules included in the simulations. The method works equally well for amino acids with neutral, acidic and basic side chains and provides estimates of the multiple pK(a) values with a mean relative error, with respect to experimental results, of 0.2 pK(a) units.

Ab Initio MD Simulations of the Bronsted Acidity of Glutathione in Aqueous Solutions: Predicting pK(a) Shifts of the Cysteine Residue

The tripeptide glutathione (GSH) is one of the most abundant peptides and the major repository for nonprotein sulfur in both animal and plant cells. It plays a critical role in intracellular oxidative stress management by the reversible formation of glutathione disulfide with the thiol-disulfide pair acting as a redox buffer. The state of charge of the ionizable groups of GSH can influence the redox couple, and hence the pK(a) value of the cysteine residue of GSH is critical to its functioning. Here we report ab initio Car-Parrinello molecular dynamics simulations of glutathione solvated by 200 water molecules, all of which are considered in the simulation. We show that the free-energy landscape for the protonation-deprotonation reaction of the cysteine residue of GSH computed using metadynamics sampling provides shift in the dissociation constant values as compared with the isolated accurate estimates of the pK(a) and correctly predicts the cysteine amino acid.

Correlation analysis of the structures and pK(a) values of carboxylic acids

The quantum-chemical descriptors were used for QSPR study of the structures of carboxylic acids and their pK(a) values. The algorithm of "Leaps and Bounds" regression was performed for selection of the variables. The CoMFA method was carried out for 3D-QSPR. As the introduction of the charge of oxygen atom(Q(2)), the results obtained by CoMFA were improved greatly.

Determination of surface pK(a) of HS(CH2)(10)COOH SAMs using an electrochemical titration method

The self-assembled monolayer(SAM) of 11-mercaptoundecanoic acid [HS(CH2)(10)COOH] was formed on a gold electrode and the effect of the charge of end group on the electrochemical response of Fe(CN)(6)(3-) at the SAM modified electrode was studied by cyclic voltammetry. At high pH, when the -COOH groups are dissociated, the current of Fe(CN)(6)(3-) is suppressed; as the solution pH is lowered, the current of Fe(CN)(6)(3-) increases. The electrochemical titration curve was obtained by correlating the currents of Fe(CN)(6)(3-) to the different pH values of electrolyte, from which the surface pK(a) was obtained to be 3. 0+/-0. 2. Furthermore, the reason of small pK(a) value was explained using SAMs of different surface coverage.

Determination of surface pK(a) of SAM using an electrochemical titration method

A self-assembled monolayer (SAM) of 3-mercaptopropionic acid (HSCH2CH2COOH) was formed on a gold electrode. The effect of the charge of the end group on the electrochemical response of Fe(CN)(6)(3-) at the SAM modified electrode was studied by using cyclic voltammetry. At high pH, when the -COOH groups are dissociated, Fe(CN)(6)(3-) current is suppressed; as the solution pH is lowered, Fe(CN)(6)(3-) current increases. The electrochemical titration curve was obtained by correlating the currents to the different electrolyte pH values, from which the surface pK(a) was obtained to be 5.2+/-0.1. Furthermore, a calculation equation was presented to simulate the electrochemical titration. As comparison, the surface pK(a) was also measured by contact angle titration as 5.6+/-0.1. The surface pK(a) values determined by the two methods in our work are consistent and accurate.