Preclinical pharmacokinetics and interspecies scaling of ragaglitazar, a novel biliary excreted PPAR dual activator

Multi-Target Drugs Against Metabolic Disorders

BACKGROUND

Metabolic disorders are a major cause of illness and death worldwide. Metabolism is the process by which the body makes energy from proteins, carbohydrates, and fats; chemically breaking these down in the digestive system towards sugars and acids which constitute the human body's fuel for immediate use, or to store in body tissues, such as the liver, muscles, and body fat.

OBJECTIVE

The efficiency of treatments for multifactor diseases has not been proved. It is accepted that to manage multifactor diseases, simultaneous modulation of multiple targets is required leading to the development of new strategies for discovery and development of drugs against metabolic disorders.

METHODS

In silico studies are increasingly being applied by researchers due to reductions in time and costs for new prototype synthesis; obtaining substances that present better therapeutic profiles.

DISCUSSION

In the present work, in addition to discussing multi-target drug discovery and the contributions of in silico studies to rational bioactive planning against metabolic disorders such as diabetes and obesity, we review various in silico study contributions to the fight against human metabolic pathologies.

CONCLUSION

In this review, we have presented various studies involved in the treatment of metabolic disorders; attempting to obtain hybrid molecules with pharmacological activity against various targets and expanding biological activity by using different mechanisms of action to treat a single pathology.

Prediction of Human Pharmacokinetics of Fomepizole from Preclinical Species Pharmacokinetics Based on Normalizing Time Course Profiles

Fomepizole is used as an antidote to treat methanol poisoning due to its selectivity towards alcohol dehydrogenase. In the present study, the goal is to develop a method to predict the fomepizole human plasma concentration versus time profile based on the preclinical pharmacokinetics using the assumption of superimposability on simulated time course profiles of animals and humans. Standard allometric equations with/without correction factors were also assimilated in the prediction. The volume of distribution at steady state (Vss) predicted by simple allometry (57.55 L) was very close to the reported value (42.17 L). However, clearance (CL) prediction by simple allometry was at least 3-fold higher to the reported value (33.86 mL/min); hence, multiple correction factors were used to predict the clearance. Both brain weight and maximum life span potential could predict the CL with 1.22- and 1.01-fold difference. Specifically, the predicted Vss and CL values via interspecies scaling were used in the prediction of series of human intravenous pharmacokinetic parameters, while the simulation of human oral profile was done by the use of absorption rate constant (Ka) from dog following the applicability of human bioavailability value scaled from dog data. In summary, the findings indicate that the utility of diverse allometry approaches to derive the human pharmacokinetics of fomepizole after intravenous/oral dosing.

Interspecies scaling of excretory amounts using allometry - retrospective analysis with rifapentine, aztreonam, carumonam, pefloxacin, miloxacin, trovafloxacin, doripenem, imipenem, cefozopran, ceftazidime, linezolid for urinary excretion and rifapentine, cabotegravir, and dolutegravir for fecal excretion

1. Interspecies allometry scaling for prediction of human excretory amounts in urine or feces was performed for numerous antibacterials. Antibacterials used for urinary scaling were: rifapentine, pefloxacin, trovafloxacin (Gr1/low; <10%); miloxacin, linezolid, PNU-142300 (Gr2/medium; 10-40%); aztreonam, carumonam, cefozopran, doripenem, imipenem, and ceftazidime (Gr3/high; >50%). Rifapentine, cabotegravir, and dolutegravir was used for fecal scaling (high; >50%). 2. The employment of allometry equation: Y = aW(b) enabled scaling of urine/fecal amounts from animal species. Corresponding predicted amounts were converted into % recovery by considering the respective human dose. Comparison of predicted/observed values enabled fold difference and error calculations (mean absolute error [MAE] and root mean square error [RMSE]). Comparisons were made for urinary/fecal data; and qualitative assessment was made amongst Gr1/Gr2/Gr3 for urine. 3. Average correlation coefficient for the allometry scaling was >0.995. Excretory amount predictions were largely within 0.75- to 1.5-fold differences. Average MAE and RMSE were within ±22% and 23%, respectively. Although robust predictions were achieved for higher urinary/fecal excretion (>50%), interspecies scaling was applicable for low/medium excretory drugs. 4. Based on the data, interspecies scaling of urine or fecal excretory amounts may be potentially used as a tool to understand the significance of either urinary or fecal routes of elimination in humans in early development.

Allometric modeling of ciclesonide, a nonhalogenated glucocorticoid, and its active metabolite, desisobutyrylciclesonide, using animal-derived pharmacokinetic parameters

Ciclesonide, a novel glucocorticosteroid, through a rapid metabolism to desisobutyryl-ciclesonide (des-ciclesonide), provides an effective treatment option for asthma episodes by the inhaled route of administration. The availability of pharmacokinetic parameters (clearance [CL/F]; volume of distribution [Vd/F]; elimination half-life [T(½)]; and elimination rate constant [Kel]) in mice, rats, rabbits, and dogs enabled the prediction of human parameter values for des-ciclesonide using the well-accepted tool of allometry after intravenous administration of ciclesonide. However, as a result of the rapid conversion of ciclesonide, it was possible to perform allometry for the CL parameter only. Simple allometry (CL = 4.781W⁰·⁷⁸⁷⁴; R² = 0.9968) appeared to predict the CL of ciclesonide in close proximity of the observed value (observed: 101.25 L/h versus predicted: 135.62 L/h). In a similar manner, simple allometry predicted the human pharmacokinetic parameters of des-ciclesonide (CL/F, Vd/F, T(½), and Kel) within a two- to threefold range of the observed values. The allometric equations for des-ciclesonide parameter values were: CL/F = 4.8166W⁰·⁴⁹² (R² = 0.8598); Vd/F = 19.052W⁰·⁶³² (R² = 0.9049); T(½) = 3.7598W⁻⁰·¹⁶¹¹(R² = 0.8551); and Kel = 0.1832W⁰·¹⁵⁹⁶ (R² = 0.8632). In conclusion, the data suggested that allometry tool may be amenable for the prediction of the pharmacokinetic parameters of des-ciclesonide despite differences in the conversion rates and bioavailability of the active metabolite in various animal species.

Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics

The high prevalence of type 2 diabetes mellitus in the world as well as the increasing reports about the adverse side effects of the existing diabetes treatment drugs have made developing new and effective drugs against the disease a very high priority. In this study, we report ten novel compounds found by targeting peroxisome proliferator-activated receptors (PPARs) using virtual screening and core hopping approaches. PPARs have drawn increasing attention for developing novel drugs to treat diabetes due to their unique functions in regulating glucose, lipid, and cholesterol metabolism. The reported compounds are featured with dual functions, and hence belong to the category of dual agonists. Compared with the single PPAR agonists, the dual PPAR agonists, formed by combining the lipid benefit of PPARα agonists (such as fibrates) and the glycemic advantages of the PPARγ agonists (such as thiazolidinediones), are much more powerful in treating diabetes because they can enhance metabolic effects while minimizing the side effects. This was observed in the studies on molecular dynamics simulations, as well as on absorption, distribution, metabolism, and excretion, that these novel dual agonists not only possessed the same function as ragaglitazar (an investigational drug developed by Novo Nordisk for treating type 2 diabetes) did in activating PPARα and PPARγ, but they also had more favorable conformation for binding to the two receptors. Moreover, the residues involved in forming the binding pockets of PPARα and PPARγ among the top ten compounds are explicitly presented, and this will be very useful for the in-depth conduction of mutagenesis experiments. It is anticipated that the ten compounds may become potential drug candidates, or at the very least, the findings reported here may stimulate new strategies or provide useful insights for designing new and more powerful dual-agonist drugs for treating type 2 diabetes.

Intravenous prediction of human pharmacokinetic parameters for ketorolac, a non-steroidal anti-inflammatory agent, using allometry approach

The intravenous pharmacokinetics data of ketorolac in mice, rats, rabbits, dogs and monkeys were assembled from literature. The relationship between the main pharmacokinetic parameters [viz., volume of distribution (V (d)) and clearance (CL)] and body weight was studied across five mammalian species, using double-logarithmic plots to predict the human pharmacokinetic parameters of CL and V (d) using simple allometry or with correction factors [maximum life span potential (MLP), brain weight, CF1 (bile flow/liver weight) and CF2 (bile flow/body weight)]. The metabolism pattern, biotransformation pathways and the predominant urinary excretion of parent and the formed metabolites of ketorolac were found to be similar amongst mice, rats, rabbits, dogs, monkeys and humans, facilitating the scaling process. The human parameter value for V (d) was predicted by simple allometric equation: 0.2481W(1.0549) (r (2) = 0.9217). The predicted V (d) value (21.92 L) is close to the reported value (17.5 L), whereas the CL was predicted by simple allometric approach or with standard correction factors viz., MLP, brain weight, CF1 and CF2. Best proximity CL value was obtained with MLP having allometric equation: 0.7126W(1.3264) (r (2) = 0.9640). The outcome of this exercise suggests that allometric scaling with suitable correction factors could potentially be used to predict the human pharmacokinetic parameters of drugs belonging to non-steroidal anti-inflammatory drugs retrospectively.