Relationship between the bioavailability and molecular properties of angiotensin II receptor antagonists

In the present study, we investigated the relationships between several molecular properties and bioavailability data for seven of the most commonly prescribed angiotensin II receptor antagonists (also known as angiotensin II receptor blockers (ARBs) or sartans), candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan and valsartan. The molecular descriptors of ARBs are:, aqueous solubility (logS values), polar surface area (PSA), molecular weight (Mw), volume value (Vol), lipophilicity (logP values) and the acidity descriptor (pK(a1)). The respective descriptors were calculated using four different software packages. The relevant bioavailability data were obtained from literature. Among calculated molecular descriptors, simple linear regression analysis showed the best correlation between bioavailability data and the lipophilicity descriptor, logP (R-2 = 0.568). Multiple linear regression established good correlations between bioavailability and the lipophilicity descriptor, logP, using the molecular weight, Mw, or the acidity descriptor, pK(a1), as an additional, independent variable (with R-2 = 0.661 and 0.682, respectively). Finally, excluding candesartan from the calculations resulted in a very good correlation (R-2 = 0.852) between the remaining ARB bioavailability and molecular descriptors MlogP and Mw as independent variables, determined by multiple linear regression

Assessment of the relationship between the molecular properties of calcium channel blockers and plasma protein binding data

In this study we investigated the relationship between the calcium channel blockers (CCBs), amlodipine, felodipine, isradipine, nicardipine, nifedipine, nimodipine, nisoldipine, verapamil and diltiazem, and their calculated molecular descriptors: polar surface area (PSA), molecular weight (Mw), volume value (Vol), aqueous solubility data (logS), lipophilicity (logP), acidity (pKa values) and plasma protein binding (PPB) data, obtained from relevant literature. The relationships between the computed molecular properties of selected CCBs and their PPB data were investigated by simple linear regression analysis that revealed very low correlations (R2 lt 0.35). When multiple linear regression (MLR) analysis was applied to investigate reliable correlations between the CCBs' calculated molecular descriptors and PPB data, the best correlations were found for the relationships between CCBs, and PPB data and lipophilicity, and with application of the molecular descriptor (Mw, Vol, or pKa) data as additional independent variables (R2=0.623; R2=0.741; R2=0.657, respectively), with an acceptable probability value (P lt 0.05), confirming that lipophilicity, together with other molecular properties, are essential for the drugs' PPB. We conclude that this could be considered as an additional in vitro approach for modeling CCBs

Evaluation of ACE inhibitors lipophilicity using in silico and chromatographically obtained hydrophobicity parameters

The aim of this study was to compare different calculation methods to determine lipophilicity, expressed as logP value, of seven ACE inhibitors (enalapril, quinapril, fosinopril, lisinopril, cilazapril, ramipril, and benazapril) with significantly different structure. Experimentally determined n-octanol/water partition coefficients, logPO/W values, were obtained from relevant literature. The correlations between all collected logP values were studied and the best agreements between calculated logP and experimentally determined logPO/W values, were observed for KOWWINlogP or MilogP values (r = 0.999 or r = 0.974, respectively). The correlations between all collected logP values and chromatographically (reversed-phase thin-layer chromatography) obtained hydrophobicity parameters, RM0 and C0, were established. The good correlations (r > 0.90) were obtained in the majority of relationships. The KOWWINlogP was established as the most suitable hydrophobicity parameter of investigated group of ACE inhibitors with r = 0.981 for correlation with RM0 and r = 0.977 for correlation with C0 parameters (water-methanol mobile phase). Using multiple linear regressions, it was established that application of two selected logP, calculated by different mathematical approaches, led to very good correlation due to the benefits of both calculation methods. The good relationships indicate that the computed logP, with careful selection of method calculation, can be useful in ACE inhibitors lipophilicity evaluation, as high-throughput screening technique

Employing machine learning to assess the accuracy of near-infrared spectroscopy of spent dialysate fluid in monitoring the blood concentrations of uremic toxins

Hemodialysis (HD) removes nitrogenous waste products from patients’ blood through a semipermeable mem- brane along a concentration gradient. Near-infrared spectroscopy (NIRS) is an underexplored method of monitoring the concentrations of several molecules that reflect the efficacy of the HD process in dialysate samples. In this study, we aimed to evaluate NIRS as a technique for the non-invasive detection of uremic solutes by assessing the correlations between the spectrum of the spent dialysate and the serum levels of urea, creatinine, and uric acid. Blood and dialysate samples were taken from 35 patients on maintenance HD. The absorption spectrum of each dialysate sample was measured three times in the wavelength range of 700-1700 nm, resulting in a dataset with 315 spectra. The artificial neural network (ANN) learn- ing technique was used to assess the correlations between the recorded NIR-absorbance spectra of the spent dialysate and serum levels of selected uremic toxins. Very good correlations between the NIR-absorbance spectra of the spent dialysate fluid with serum urea (R=0.91) and uric acid (R=0.91) and an excellent correlation with serum creatinine (R=0.97) were obtained. These results support the application of NIRS as a non-invasive, safe, accurate, and repetitive technique for online monitoring of uremic toxins to assist clinicians in assessing HD efficiency and individualization of HD treatments

Assessment of the relationship between the molecular properties of calcium channel blockers and plasma protein binding data

In this study we investigated the relationship between the calcium channel blockers (CCBs), amlodipine, felodipine, isradipine, nicardipine, nifedipine, nimodipine, nisoldipine, verapamil and diltiazem, and their calculated molecular descriptors: polar surface area (PSA), molecular weight (Mw), volume value (Vol), aqueous solubility data (logS), lipophilicity (logP), acidity (pKa values) and plasma protein binding (PPB) data, obtained from relevant literature. The relationships between the computed molecular properties of selected CCBs and their PPB data were investigated by simple linear regression analysis that revealed very low correlations (R2<0.35). When multiple linear regression (MLR) analysis was applied to investigate reliable correlations between the CCBs’ calculated molecular descriptors and PPB data, the best correlations were found for the relationships between CCBs, and PPB data and lipophilicity, and with application of the molecular descriptor (Mw, Vol, or pKa) data as additional independent variables (R2=0.623; R2=0.741; R2=0.657, respectively), with an acceptable probability value (P<0.05), confirming that lipophilicity, together with other molecular properties, are essential for the drugs’ PPB. We conclude that this could be considered as an additional in vitro approach for modeling CCBs. [Projekat Ministarstva nauke Republike Srbije, br. TR34031

Relationship between the bioavailability and molecular properties of angiotensin II receptor antagonists

In the present study, we investigated the relationships between several molecular properties and bioavailability data for seven of the most commonly prescribed angiotensin II receptor antagonists (also known as angiotensin II receptor blockers (ARBs) or sartans), candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan and valsartan. The molecular descriptors of ARBs are:, aqueous solubility (logS values), polar surface area (PSA), molecular weight (Mw), volume value (Vol), lipophilicity (logP values) and the acidity descriptor (pKa1). The respective descriptors were calculated using four different software packages. The relevant bioavailability data were obtained from literature. Among calculated molecular descriptors, simple linear regression analysis showed the best correlation between bioavailability data and the lipophilicity descriptor, logP (R2 = 0.568). Multiple linear regression established good correlations between bioavailability and the lipophilicity descriptor, logP, using the molecular weight, Mw, or the acidity descriptor, pKa1, as an additional, independent variable (with R2 0.661 and 0.682, respectively). Finally, excluding candesartan from the calculations resulted in a very good correlation (R2 = 0.852) between the remaining ARB bioavailability and molecular descriptors MlogP and Mw as independent variables, determined by multiple linear regression. [Projekat Ministarstva nauke Republike Srbije, br. TR34031

The effect of the molecular properties of calcium channel blockers on their elimination route

Calcium channel blockers (CCBs) are among the most widely used drugs in cardiovascular medicine. In this study, nine CCBs (amlodipine, felodipine, isradipine, nicardipine, nifedipine, nimodipine, nisoldipine, verapamil and diltiazem) were investigated to assess the relationship between their molecular properties and elimination data obtained from literature. The descriptors of the molecular properties of CCBs were calculated using three software packages. The relationship between computed molecular properties and elimination data collected from relevant literature, initially investigated with simple linear regression analysis, showed poor correlation (R2 <0.25). Application of molecular weight or volume data as additional independent variable, multiple linear regression (MLR) revealed better correlations (R2 ~ 0.38) between CCB renal and fecal elimination data and their lipophilicity. Excluding nimodipine from the calculations resulted in more acceptable correlations. The best correlations were established after computed lipophilicity descriptor and molecular weight were applied (R2 = 0.66 with acceptable probability value). [Projekat Ministarstva nauke Republike Srbije, br. TR34031

The influence of certain molecular descriptors of fecal elimination of angiotensin II receptor antagonists

Angiotensin II receptor antagonists (ARBs) modulate the function of the renin-angiotensin-aldosterone system and are commonly prescribed antihypertensive drugs, especially in patients with renal failure. In this study, the relationship between several molecular properties of seven ARBs (candesartan, eprosartan, irbesartan, losartan, olmesartan, telmisartan, valsartan) and their fecal elimination data obtained from the literature were investigated. The ARB molecular descriptors were calculated using three software packages. Simple linear regression analysis showed the best 2 correlation between fecal elimination data and lipophilicity descriptor, ClogP values (R2 = 0.725). Multiple linear regression was applied to examine the correlation of ARBs’ fecal elimination data with their lipophilicity and one additional, calculated descriptor. The best correlation (R2 = 0.909 with an acceptable probability value, P <0.05) was established between the ARB fecal elimination data and their lipophilicity and aqueous solubility data. Applying computed molecular descriptors for evaluating drug elimination is of great importance in drug research

Estimation of plasma protein binding of selected antipsychotics using computed molecular properties

The plasma protein binding (PPB) data of twelve antipsychotics (aripiprazole, clozapine, olanzapine, quetiapine, risperidone, sertindole, ziprasidone, chlorpromazine, flupentixol, fluphenazine, haloperidol, zuclopenthixol) were estimated using computed molecular descriptors, which included the electronic descriptor – polar surface area (PSA), the constitutional parameter – molecular weight (Mw), the geometric descriptor – volume value (Vol), the lipophilicity descriptor (logP) and aqueous solubility data (logS), and the acidity descriptor (pKa). The relationships between computed molecular properties of the selected antipsychotics and their PPB data were investigated by simple linear regression analysis. Low correlations were obtained between the PPB data of the antipsychotics and PSA, Mw, Vol, pKa, logS (R <0.30) values, while relatively higher correlations (0.35 < R2 < 0.70) were obtained for the majority of logP values. Multiple linear regression (MLR) analysis was applied to access reliable correlations of the PPB data of the antipsychotics and the computed molecular descriptors. Relationships with acceptable probability values (P<0.05) were established for five lipophilicity descriptors (logP values) with application of the acidity descriptor (pKa) as independent variables: AlogP (R2=0.705), XlogP3 (R2=0.679), ClogP (R2=0.590), XlogP2 (R2=0.567), as well as for the experimental lipophilicity parameter, logPexp (R2=0.635). The best correlations obtained in MLR using AlogP and pKa as independent variables were checked using three additional antipsychotics: loxapine, sulpiride and amisulpride, with the PPB values of 97%, “less than” 40% and 17%, respectively. Their predicted PPB values were relatively close to the literature data. The proposed technique confirmed that lipophilicity, together with acidity significantly influences the PPB of antipsychotics. The described procedure can be regarded as an additional in vitro approach to the modeling of the investigated group of drugs. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 172016

Cancer antigen 125 concentrations in patients on chronic peritoneal dialysis: Relationship with dialysis quality and membrane transport properties

The aim of this study was to evaluate longitudinal changes in drained dialysate cancer antigen 125 (dCA-125) levels and to assess relationships between dCA-125 and dialysis quality, peritoneal membrane transport rates, dialysate glucose load, peritonitis and use of erythropoiesis stimulating agents (ESA), angiotensin-converting-enzyme inhibitors (ACEi) and statins in patients with end-stage renal failure during the first 6 months of peritoneal dialysis (PD) treatment. This prospective study included 20 patients (11 males and 9 females; mean age 62.90±12.69 years) who were followed-up during the first 6 months of PD using conventional low pH glucose-based dialysis fluids. The concentration of dCA-125 was measured in all patients, and the peritoneal equilibration test (PET), peritoneal dialysis treatment adequacy (Kt/V), normalized protein catabolic rate (nPCR), and total, peritoneal and residual clearances of urea and creatinine were calculated. Information on peritonitis occurrence, the use of ESA, ACEi and statins were collected. Data were analyzed by the Mann-Whitney test, Wilcoxon matched pairs test and Spearman’s rank correlation. The concentration of dCA-125 significantly decreased during the follow-up (p=0.016). After 6 months of PD treatment, the concentration of dCA-125 decreased significantly (p=0.016) in all patients. The decrease was present in all patients, but was statistically significant in patients on ACEi therapy (p=0.006) and in patients not using statins (p=0.005) or ESA (p=0.012). No correlation was found between dCA-125 and glucose load, but a statistically significant negative correlation between dCA-125 and the PET for creatinine was observed (p=0.013). These findings challenge the role of dCA-125 in predicting mesothelial cell integrity in PD patients. [Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 145070