{"id":43147,"date":"2022-03-31T10:02:12","date_gmt":"2022-03-31T10:02:12","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=43147"},"modified":"2022-05-10T10:15:17","modified_gmt":"2022-05-10T10:15:17","slug":"advantages-of-allometric-scaling-methods-for-predicting-human-pharmacokinetics-of-novel-jak-inhibitor-baricitinib-and-dose-extrapolation","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol15no1\/advantages-of-allometric-scaling-methods-for-predicting-human-pharmacokinetics-of-novel-jak-inhibitor-baricitinib-and-dose-extrapolation\/","title":{"rendered":"Advantages of Allometric Scaling Methods for Predicting Human Pharmacokinetics of Novel JAK Inhibitor -Baricitinib and Dose Extrapolation"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

The novel JAK inhibitors tap down cytokine mediated signal via the JAK-STAT pathway, these have crucial role in immune regulation and growth. These ‘small molecule’ drugs are highly\u00a0specific for blocking targets identified within cells that cause chronic inflammation 1<\/sup>. Rheumatoid arthritis is categorized as autoimmune disease and occurred in females than males\u00a0and more frequently in elder population. The occurrence rate mentioned in year 2002 ranged from 0.5% to 1% 2<\/sup><\/p>\n

Though allometric scaling methods needs supplementary refinements and has certain limits, it is still considered as potential tool and balanced option for the estimate of pharmacokinetic\u00a0parameters in species for which there are no data reported or to get good interpret preclinical efficacy and safety trials 3<\/sup>. Rheumatoid arthritis is a devastating disease that affects the quality\u00a0of life and efficiency of millions worldwide. This disease is characterized by inflammation of the joints that leads to damage to the cartilage 4<\/sup>.<\/p>\n

Baricitinib have anti-inflammatory, immunomodulatory and antineoplastic activities. It binds to JAK1\/2 and inhibitsits activation and leads to the inhibition of the JAK-STAT signaling\u00a0pathways. This inhibition lowers the production of inflammatory cytokines and prevents an inflammatory response and reduces tumor cell growth.\u00a0Baricitinib approved for the treatment of moderate- to-severely rheumatoid arthritis in adults and was hypothesized to be a better therapeutic option for COVID\u201319. It was measured amongst all\u00a0molecules studied to have a four role by inhibiting relevant cytokine signaling and have activity against NAKs, AAK1, and GAK 2,3 , which stimulate AP\u20132-linked host viral propagation 5\u20137<\/sup>.<\/p>\n

Approved by the FDA for RA in June 2018 However, currently in phase 2 trials for psoriasis and AD 8<\/sup><\/p>\n\n\n\n
\"Vol15No1_Adv_Sad_fig1\"<\/a><\/td>\nFigure 1: Structure of baricitinib<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

BenevolentAI identified this compound as a prospective treatment for COVID-19 in its\u00a0AI-based hypothesis.\u00a0Data from Eli Lilly\u2019s trial\u00a0showed that baricitinib significantly reduces mortality in hospitalized COVID-19 patients by 38%.\u00a0 Review of the literature suggested that allometry scaling of novel\u00a0JAK inhibitor Baricitinib hasn\u2019t been published.<\/p>\n

Methods<\/strong><\/p>\n

We used literature published database to find relevant articles of baricitinib for allometry scaling\u00a0exercise. Baricitinib preclinical (Rat, Beagle dogs and Monkey) and clinical data published were\u00a0considered for allometry predictions. The observed human pharmacokinetic data were also\u00a0gathered in order to enable the comparison of the predicted vs. observed pharmacokinetic data.\u00a0The published intravenous data of baricitinib in three preclinical species were used for the\u00a0allometry scaling study of baricitinib. The correlation between the main primary PK parameters\u00a0[volume of distribution (Vd<\/sub>) and clearance (Cl)] and body weight (BW) was studied across three\u00a0pre-clinical species by using double logarithmic plots for prediction of the human\u00a0pharmacokinetic parameters of Cl and Vd<\/sub> using simple allometry methods.<\/p>\n

The collected data of volume of distribution (Vd<\/sub>) and clearance (Cl) values were transformed\u00a0into L and mL\/min, respectively. Table 1 shows the PK parameters collected from published\u00a0literature from three animal species (Rat, beagle dogs and Monkeys) that were used in this allometric scaling exercise.<\/p>\n

The published studies indicate that the total body clearance is ~17 L\/h and the renal clearance\u00a0contribution is ~13.4 L\/h in human healthy subjects; it showed that parent fraction of baricitinib\u00a0is predominantly excreted via urine. The literature showed the major transporters involvement of P-gp, OAT-3 and MATE2-K for renal excretion 9<\/sup>.<\/p>\n

Table 1: Preclinical PK parameters of baricitinib from published data\u00a0<\/strong><\/p>\n\n\n\n\n\n\n
Species <\/strong><\/td>\nBody Wt.(Kg)<\/strong><\/td>\nAUC0-t <\/sub>(hr*ng \/mL)<\/strong><\/td>\nT\u00bd <\/strong><\/p>\n

(h)<\/strong><\/td>\n

Cl<\/strong><\/p>\n

(mL\/min\/Kg)<\/strong><\/td>\n

Vd<\/sub> (<\/strong>L\/Kg.)<\/strong><\/td>\n<\/tr>\n
SD Rat <\/strong><\/td>\n0.204<\/td>\n134<\/td>\n1.16<\/td>\n21.6<\/td>\n2.10<\/td>\n<\/tr>\n
Beagle dogs<\/strong><\/td>\n10.0<\/td>\nNA<\/td>\n3.47<\/td>\n6.66<\/td>\n1.40<\/td>\n<\/tr>\n
Cynomolgus
\nMonkeys<\/strong><\/td>\n		8.00<\/td>\nNA<\/td>\nNA<\/td>\n5.99<\/td>\n1.10<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

PK Data from EMA assessment report [9]; NA: Not available.<\/p>\n

Simple allometry: The scaling of Vd<\/sub> and Cl was carried out as per below equations.<\/p>\n

Vd<\/sub> = aWx\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026.(1)<\/p>\n

Cl = bWy\u2026\u2026\u2026\u2026\u2026\u2026\u2026\u2026(2)<\/p>\n

Where, W is the body weight in kg of all species.<\/p>\n

a and b are the coefficients and x and y are exponents of allometry.<\/p>\n

The pharmacokinetic parameter (Vd<\/sub> or Cl) and W (Body Weight) were converted logarithmically and fitted to the below equation:<\/p>\n

log (Vd<\/sub> or Cl) = log a + b log W, by linear least-square regression analysis.<\/p>\n

The correction factors applied in this study are with consideration of mathematically derived\u00a0constants like maximum life span potential (MLP), brain weight (BW) as well as the\u00a0physiological process like kidney blood flow (KBF), Monkey liver blood flow (MLBF) and\u00a0excretory mechanisms as bile correction factor; Glomerular filtration rate (GFR).<\/p>\n

Inclusion of correction factors (CF): These CF was summarized and was included into allometric equations are as below equation:<\/p>\n

log (Cl \u00d7 MLP\/BrW\/GFR\/KBF\/bile correction) = log b + y log W \u2026\u2026\u2026\u2026\u2026 (3)<\/p>\n

Human Cl (Predicted) = Monkey Cl \u00d7 (Human LBF\/Monkey LBF) \u2026\u2026\u2026\u2026.. (4)<\/p>\n

The LBF method for prediction of human clearance (Cl) from each of the pre-clinical species as a fraction of liver blood flow as follows:<\/p>\n

Human Cl (Predicted) = Animal clearance * (Human LBF\/animal LBF) …(5)\u00a0 [10]\n\n\n\n
\"Vol15No1_Adv_Sad_fig2\"<\/a><\/td>\nFigure 2: Utility of allometric scaling in drug development.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The allometric scaling of the clearance (Cl); the physiological parameters were taken into\u00a0consideration like as brain weight (BrW) or maximum lifespan potential (MLP) of each species\u00a0were included as correction factor. The values for body weight, Brain weight and MLP used in\u00a0for the exercise are presented in Table 2.<\/p>\n

Table 2: Body weight, Brain wt. and MLP values considered for allometry\u00a0\u00a0\u00a0\u00a0\u00a0<\/strong><\/p>\n\n\n\n\n\n\n\n
Species<\/strong><\/td>\nWeight (Kg)<\/strong><\/td>\nBrain wt (Gm) <\/strong><\/td>\nMaximum lifespan potential MLP (Years)<\/strong><\/td>\n<\/tr>\n
Rat<\/strong><\/td>\n0.240<\/strong><\/td>\n1.80<\/td>\n4.68<\/td>\n<\/tr>\n
Dog <\/strong><\/td>\n10.0<\/strong><\/td>\n80.0<\/td>\n19.7<\/td>\n<\/tr>\n
Monkey <\/strong><\/td>\n8.00<\/strong><\/td>\n100<\/td>\n8.01<\/td>\n<\/tr>\n
Human <\/strong><\/td>\n70.0<\/strong><\/td>\n1400<\/td>\n93.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Digitalization of reported pharmacokinetic data<\/strong><\/p>\n

The reported Phase-I patients pharmacokinetic data of Baricitinib (Xia Zhaoet al.,<\/em> 2020);11<\/sup> was collected from phoenix WinNonlin 8.1 using graph reader software as graph of time vs.\u00a0plasma concentration was reported and no individual plasma concentrations was published.\u00a0(Available at www.grpahreader.com ; accessed on 02 May. 2021) and Phoenix WinNonlin 8.1\u00a0software (Pharsight, Mountain View, CA, USA).<\/p>\n

Prediction of intravenous plasma vs. time profiles of baricitinib in humans<\/strong><\/p>\n

Baricitinib plasma concentrations vs. time profile were derived by using simulation using\u00a0Phoenix WinNonlin 8.1 software. The simulation of human intravenous profile were carried out\u00a0at 2 mg\/ kg dose employing one compartment model with bolus input (using WNL5 classic modeling, PK model #1).The pharmacokinetics of baricitinib in humans was characterized by\u00a0rapid absorption with a long apparent elimination half-life; the decrease in plasma concentrations displayed a biphasic profile (Xia Zhaoet al.,<\/em> 2020).<\/p>\n

Table 3: Comparative human digitalized values from graphreader with the reported values (Dose-2 mg\/Kg)<\/strong><\/p>\n\n\n\n\n\n\n
PK parameters<\/strong><\/td>\nPredicted<\/strong><\/td>\nReported*<\/strong><\/td>\n<\/tr>\n
T max<\/sub> (h)<\/td>\n1.00<\/td>\n1.00 (0.50-1.50)<\/td>\n<\/tr>\n
Cmax<\/sub> (ng\/mL)<\/td>\n23.7<\/td>\n24.6 (4.11)<\/td>\n<\/tr>\n
AUCinf,obs<\/sub> (ng\u00d7 h\/mL)<\/td>\n138<\/td>\n140 (15.3)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

*Source: Xia Zhao et al.,<\/em> 2020<\/p>\n

Dose extrapolation<\/strong><\/p>\n

The retrospective analyses of baricitinib dose were done with applications of different dose extrapolations methods as follows:<\/p>\n

As per FDA draft guidelines (USFDA, 2005) the dose by factor approach was applied using translation of animal doses to human equivalent doses.<\/p>\n

Table 4: Animal equivalent dose factors of different species BSA-CS 12<\/sup><\/strong><\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Species<\/strong><\/td>\nBSA-CS (Divide human dose by)<\/strong><\/td>\n<\/tr>\n
Mouse<\/strong><\/td>\n0.081<\/td>\n<\/tr>\n
Hamster<\/strong><\/td>\n0.135<\/td>\n<\/tr>\n
Rat<\/strong><\/td>\n0.162<\/td>\n<\/tr>\n
Guinea pig<\/strong><\/td>\n0.216<\/td>\n<\/tr>\n
Rabbit<\/strong><\/td>\n0.324<\/td>\n<\/tr>\n
Dog<\/strong><\/td>\n0.541<\/td>\n<\/tr>\n
Monkey (Cynomolgus, Rhesus)<\/strong><\/td>\n0.324<\/td>\n<\/tr>\n
Marmoset<\/strong><\/td>\n0.162<\/td>\n<\/tr>\n
Baboon<\/strong><\/td>\n0.541<\/td>\n<\/tr>\n
Micro-pig<\/strong><\/td>\n0.730<\/td>\n<\/tr>\n
Mini-pig<\/strong><\/td>\n0.946<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Application of Rat NOAEL<\/strong><\/p>\n

As per published data (CDER, report 2018) reported NOAEL in rat was 8 mg\/Kg; with application of BSA correction factor and safety factor the calculated HED the predicted dose was 47 mg in human.<\/p>\n

HED =NOAEL of most sensitive species (mg=kg) x BSA -CF x 60 (kg)<\/p>\n

Application of Mice NOAEL<\/strong><\/p>\n

Mice NOAEL reported value is 300 mg\/Kg; with application of BSA correction factor and safety factor the calculated HED was 1458 mg in human.<\/p>\n

Application of <\/strong>exponent for body surface area (0.67)<\/strong><\/p>\n

By use of this method which applies an exponent for BSA (0.67) and consider for difference in metabolic rate (MR), to translate doses between animals and humans. Thus, HED is derived by the equation considering dog NOAEL<\/p>\n

HED (mg \/ kg) = Animal NOAEL (mg \/ kg) \u00d7 (Weight animal<\/sub>[ kg]\/Weight Human<\/sub> [ kg] (1-0.67)<\/sup><\/p>\n

The calculated dose by this method was 0.132 mg\/Kg; for 60 Kg it was 8.0 mg\/Kg; which was 2 fold higher than actual clinical dose.<\/p>\n

Pharmacokinetically guided approach<\/strong><\/p>\n

Starting dose = AUC in index species X Estimated clearance in human<\/p>\n

The AUC obtained at the NOAEL of dog (index species) reported AUC0-t (\u00b5M*hr) -1.21 and estimated clearance in human (L)-58.14<\/p>\n

=1.21 X 58.14=70.18 mg<\/p>\n

With application of safety factor of 10; the starting dose calculated was 7.0 mg for human 19<\/sup><\/p>\n

Overall the PK guided approach and exponent for BSA based approach was found closer to actual human dose of 4.0 mg\/Kg.<\/p>\n

Table 5: Evaluations of scaling methods<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n
Parameter<\/strong><\/td>\nTypes of Scaling<\/strong><\/td>\nPredicted values<\/strong><\/td>\nReported values<\/strong><\/td>\nFold differences<\/strong><\/td>\n<\/tr>\n
Vd<\/sub> (L)<\/td>\nAllometric scaling by simple method (for Vd<\/sub>)<\/td>\n65.3<\/td>\n75.7<\/td>\n0.86<\/td>\n<\/tr>\n
Cl (mL\/ min)<\/td>\nSimple allometry<\/td>\n231<\/td>\n245<\/td>\n1.06<\/td>\n<\/tr>\n
\u00a0MLP correction factor<\/td>\n405<\/td>\n0.61<\/td>\n<\/tr>\n
Brain weight (B.W) correction factor<\/td>\n467<\/td>\n0.52<\/td>\n<\/tr>\n
Glomerular filtration rate (GFR) correction factor<\/td>\n145<\/td>\n1.69<\/td>\n<\/tr>\n
Kidney blood flow (KBF) correction factor<\/td>\n180<\/td>\n0.74<\/td>\n<\/tr>\n
Bile correction factor<\/td>\n627<\/td>\n0.39<\/td>\n<\/tr>\n
Monkey liver blood flow<\/td>\n200<\/td>\n0.81<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Table 6: Prediction of Cl based on LBF method (equation 5).<\/strong><\/p>\n\n\n\n\n\n
Species<\/strong><\/td>\nPredicted human Cl (mL\/min)<\/strong><\/td>\nActual reported Cl of Human (mL\/min)<\/strong><\/td>\nFold difference<\/strong><\/td>\n<\/tr>\n
Rat<\/td>\n551<\/td>\n245<\/td>\n0.44<\/td>\n<\/tr>\n
Dog<\/td>\n311<\/td>\n0.79<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Table 7: Free fraction data values of preclinical species extracted from literature<\/strong><\/p>\n\n\n\n\n\n\n\n
Species<\/strong><\/td>\nFree fraction in serum (%)<\/strong><\/td>\n<\/tr>\n
Rat<\/td>\n47<\/td>\n<\/tr>\n
Dog<\/td>\n61<\/td>\n<\/tr>\n
Monkey<\/td>\n49<\/td>\n<\/tr>\n
Human<\/td>\n50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

*Source: EMA assessment report 9<\/sup><\/p>\n

Result and discussion<\/strong><\/p>\n

In order to understand the PK (Pharmacokinetics) predictions in human and providing an opportunity to refine various allometric scaling predictions this exercise will guide the scientist\u00a0from drug discovery to development.<\/p>\n

A simple allometry correlation was found to be satisfactory for the prediction of intravenous\u00a0human clearance (Cl)\/volume of distribution (Vd<\/sub>) for baricitinib. The excellent correlation was\u00a0observed between monkey and human as monkey liver blood flow method predicted the Cl value\u00a0with 0.81- fold difference; the application of Cl based on LBF methods showed that higher\u00a0species have better correlation than rat.<\/p>\n

The predicted values of clearance observed were within 2.0 fold of values by simple allometry this showed success of allometry scaling methods for baricitinib.<\/p>\n

Based on our study and reviewed literature it is well identified that volume of distribution can be predicted using simple allometric scaling; still, the clearance was not establishing to be agreeable\u00a0for simple allometry and therefore, from long time correction factors has been proposed and in many cases the use of such correction factors (CF) has led to improved predictions of clearance\u00a0and half-life.\u00a0 (Mahmood I.) 13<\/sup> Studied and clarified the significance of correction factors that may further convey allometry as a proficient predictive tool. Therefore, in this study we explored\u00a0advantages of these methods which were not applied for novel JAK inhibitors.<\/p>\n

The application of simple allometry resulted in a predicted Cl value of 231 mL\/min, which was\u00a0close agreement (1.06) with reported human value of 245 mL\/min. The use of correction factors\u00a0(CF) was generally considered to provide a better estimate of the human Cl.<\/p>\n

The application of correction factors (CF) as bile flow and glomerular filtration rate of each\u00a0species can also be applicable for drug classes that show similarity in the drug disposition as\u00a0compared to baricitinib. In agreement, the inclusion of GFR, KBF and bile correction factor\u00a0resulted in prediction of 0.74 -fold, 1.69-fold and 0.39 fold respectively; lower Cl value by KBF\u00a0and GFR methods and higher by biliary clearance method than the reported human value.<\/p>\n

In the absence of human PK data the application of monkey liver blood flow (MLBF) has been\u00a0proved as better tool for getting a good correlation to the human values as monkey have better\u00a0correlation to human. The application of species specific liver blood flow led to the prediction of\u00a0human Cl within 0.81-fold to the reported value.<\/p>\n

The prediction of human Vd<\/sub> of baricitinib by simple allometry resulted in an exponent of 0.85,\u00a0which is within the satisfactory range of 0.8-1.10 the similar results were observed in the\u00a0literature for most of the compounds; this resulted in a predicted value of 65.3 L which was\u00a0observed in close of the observed human value of \u00a075.7 L.<\/p>\n

The additional efforts were tried to find out impact of plasma protein binding factors (Table 7) and observed that the prediction was not improved for simple allometry and for rest of the\u00a0methods; as protein binding are not different form rodent to human. The many literature studies showed that protein binding factor were successful for vertical allometry and drugs with high extraction rate.\u00a0Dose extrapolation study showed that PK guided approach and exponent for BSA based approach was found to predict FIH dose within two fold of 4.0 mg\/Kg.<\/p>\n\n\n\n
\"Vol15No1_Adv_Sad_fig3\"<\/td>\nFigure 3: Prediction of human Vd<\/sub> by Simple allometric scaling using rat,\u00a0\u00a0dog and monkey data.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol15No1_Adv_Sad_fig4\"<\/a><\/td>\nFigure 4: Human Cl prediction by allometry from preclinical species (a) simple allometry (b) MLP correction factor (c) BW. correction factor (d) GFR correction factor (e) KBF correction factor (f) Bile flow correction factor<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol15No1_Adv_Sad_fig5\"<\/a><\/td>\nFigure 5: Comparison of multiple simulation methods with the reported human pharmacokinetics profile of baricitinib at 2 mg\/kg<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Conclusion<\/strong><\/p>\n

Retrospective assessment of human pharmacokinetics prediction is necessary for improving prediction techniques and strategies for future candidate drugs.<\/p>\n

Allometric scaling retrospectively predicted Vd<\/sub> and Cl parameters well within two fold of observed values. The overall PK of the baricitinib predicted with excellent accuracy with few\u00a0exceptions of biliary corrections factor and could be used as a forthcoming tool for this class of drugs.<\/p>\n

The correction factors evaluated in this study of baricitinib can be applicable for other or similar classes of drugs having similar metabolism and excretion pathways observed for baricitinib.<\/p>\n

The reason for over prediction of biliary clearance may be due to lower recovery of baricitinib in feces as reported in published articles (Baricitinib recovery 15 % in feces by Sarah et al<\/em> 2020) 14<\/sup>.<\/p>\n

Allometry scaling will continue a method of choice for the prediction human PK parameters and the selection of first in human dose based solely on existing PK data of preclinical species.<\/p>\n

Allometric scaling approaches will be decisive tool for scientists to optimize doses among species from preclinical to clinical stage of development.<\/p>\n

Acknowledgement<\/strong><\/p>\n

None<\/p>\n

Conflict of interest<\/strong><\/p>\n

The authors wish to declare that there are no conflicts of interests in the contents of the manuscript.<\/p>\n

Funding Sources<\/strong><\/p>\n

There is no funding sources.<\/p>\n

References<\/strong><\/p>\n

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