Application of Allometric Scaling and Salisbury Rule for the Prediction of Antimalarial Drugs for First-in-Pediatric Dose Selection.

BACKGROUND: In pediatric drug development, the selection of first-in-pediatric dose is of immense importance. Generally, the pharmacokinetic information and a safe and efficacious dose of a drug in adults are already known and this information can then be used to select first-in-pediatric dose. The objective of this study was to predict the pediatric dose of antimalarial drugs and compare the predicted dose with the recommended dose. METHODS: In this study, two simple methods to project a first-in-pediatric dose to initiate a clinical trial for antimalarial drugs were evaluated. These two methods were Salisbury Rule and allometric scaling. The predicted doses of antimalarial drugs by the two methods were compared with the observed doses recommended by the World Health Organization (WHO) or the US Food and Drug Administration (FDA). RESULTS: In this study, 15 antimalarial drugs with 88 observations (different body weight groups) were evaluated. From allometric scaling, all 88 observations were within 0.5-1.5-fold and 0.7-1.3-fold prediction error. From Salisbury Rule, all 88 observations were within 0.5-1.5-fold and 86 observations were within 0.7-1.3-fold prediction error. CONCLUSIONS: The proposed methods are simple and quite accurate in their predictive power. These methods can be developed on a spreadsheet or a calculator in a very short period of time and are applicable to first-in-pediatric clinical trials or even in a clinical setting.

Pharmacokinetics of antibodies during Pregnancy: Impact of pregnancy on the pharmacokinetics of antibodies (Part 2).

In Part 1, we provided a general description of macromolecules, pharmacokinetics (PK) characteristics in non-pregnant subjects, and the physiological changes during pregnancy. Here we further elaborate on the impact of pregnancy on the PK of antibodies through illustrative case studies (immunoglobulins, infliximab, adalimumab and eculizumab). Using published data from nonclinical and clinical studies, we present measured or calculated PK parameters from pregnant subjects comparing with data from non-pregnant subjects, if available. Due to the paucity of PK data evaluating PK of antibodies during pregnancy, we also provide examples of PK studies for small molecules. Finally, we draw conclusions on the nature and direction of PK changes for both antibodies and small molecules as well as provide recommendations for areas that would benefit from further studies.

Pharmacokinetics of antibodies during pregnancy: General pharmacokinetics and pregnancy related physiological changes (Part 1).

Pharmacokinetics (PK) studies are important to determine a safe and effective dose of both small and large molecule drugs. Intrinsic factors such as pregnancy can substantially alter the PK of a drug. Several PK studies have been published for small molecules administered during pregnancy, but such investigations are scarce for macromolecules including monoclonal and polyclonal antibodies. In this part 1 of 2 reviews, we first provide a general description of macromolecule drugs, the PK differences with small molecules, and current knowledge on their absorption, distribution, metabolism and elimination in non-pregnant subjects. We then review in detail the physiological changes during pregnancy. While some of the physiologic adaptions of pregnancy, for example increased plasma volume and cardiac output, are expected to impact PK of antibody therapeutics, the effects of others, such as increased GFR and altered immune responses are not fully understood. We conclude that further investigations are needed to fully elucidate how pregnancy can impact PK properties of macromolecules.

A comparison of different methods for the first-in-pediatric dose selection.

Background and Aim: To conduct a pediatric clinical trial, it is important to optimize pediatric dose as accurately as possible. This is mainly because due to ethical reasons, children cannot be given several doses to evaluate pharmacokinetics, safety, and efficacy of a drug. Methods: In this study, several simple methods to project a first-in-pediatric dose to initiate a clinical trial were evaluated. These methods were as follows:(1) Weight-based pediatric dose prediction (allometric scaling), (2) Salisbury rule (weight-based method), and (3) pediatric dose prediction based on predicted clearance. These methods were compared with the dose given to children in clinical practice. The methods were also compared with whole-body physiologically based pharmacokinetic (PBPK) model (n = 11). A ±30% prediction error (predicted vs. observed) was considered acceptable. Results: There were 27 drugs with 113 observations (different age groups from preterm neonates to adolescents). At least, ≤30% prediction error in pediatric dose projection was noted for more than 70% observations. The predictive performance of all the proposed methods was comparable with the whole-body PBPK. Conclusions: The proposed methods are simple and accurate and can be developed on a spreadsheet in a very short period of time. Relevance for Patients: The study provides an estimate of first-in-pediatric dose by simple methods to initiate pediatric clinical trials. Especially, Salisbury rule is based on body weight and is very simple and works fairly well in children >30 kg body weight and can be even used in clinical settings.

Prediction of total and renal clearance of renally secreted drugs in neonates and infants (≤3 months of age).

Background: Renal excretion is a major route of elimination for many drugs. Renal clearance is the sum of three processes: glomerular filtration, tubular secretion, and tubular re-absorption. Tubular secretion is an active transport process and is immature at birth. In the neonates, renal tubular secretion can be important for the elimination of those drugs which are renally secreted, such as penicillins and cephalosporins. Aim: The objective of this study was to evaluate the predictive performances of three models to predict total and renal clearance of renally secreted drugs in neonates (≤3 months of age). Methods: From the literature, clearance values for 12 renally secreted drugs for neonates and adults were obtained. Three models were used to predict the clearances of these drugs. The predictive performances of these models were evaluated by comparing the predicted values of total and renal clearance with the observed clearance values in the neonates. Results: There were 12 drugs with 22 observations (preterm and term neonates, ≤3 months of age) for total clearance and six drugs with eight observations for renal clearance. For both total and renal clearance, a prediction error of <50% was observed by all three models evaluated in this study. Conclusions: The proposed models can predict mean total and renal clearances of renally secreted drugs in preterm and term neonates (≤3 months of age) with reasonable accuracy (50% prediction error) and are of practical value during neonatal drug development. Relevance for Patients: The work may help in dose selection for neonates for medicines that are renally secreted.

A Simple Method for the Prediction of Therapeutic Proteins (Monoclonal and Polyclonal Antibodies and Non-Antibody Proteins) for First-in-Pediatric Dose Selection: Application of Salisbury Rule.

In order to conduct a pediatric clinical trial, it is important to optimize pediatric dose as accurately as possible. In this study, a simple weight-based method known as 'Salisbury Rule' was used to predict pediatric dose for therapeutic proteins and was then compared with the observed pediatric dose. The observed dose was obtained mainly from the FDA package insert and if dosing information was not available from the FDA package insert then the observed dose was based on the dose given to an age group in a particular study. It was noted that the recommended doses of most of the therapeutic proteins were extrapolated to pediatrics from adult dose based on per kilogram (kg) body weight basis. Since it is widely believed that pediatric dose should be selected based on the pediatric clearance (CL), a CL based pediatric dose was projected from the following equation: Dose in children = Adult dose × (Observed CL in children/Observed adult CL). In this study, this dose was also considered observed pediatric dose for comparison. A ±30% prediction error (predicted vs. observed) was considered acceptable. There were 21 monoclonal antibodies, 5 polyclonal antibodies in children ≥ 2 years of age, 4 polyclonal antibodies in preterm and term neonates, and 11 therapeutic proteins (non-antibodies) in the study. In children < 30 kg body weight, the predicted doses were within 0.5−1.5-fold prediction error for 87% (monoclonal antibody), 100% (polyclonal antibody), and 92% (non-antibodies) observations. In children > 30 kg body weight, the predicted doses were within 0.5−1.5-fold prediction error for 96% (monoclonal antibody), 100% (polyclonal antibody), and 100% (non-antibodies) observations. The Salisbury Rule mimics more to CL-based dose rather than per kg body weight-based extrapolated dose from adults. The Salisbury Rule for the pediatric dose prediction can be used to select first-in-children dose in pediatric clinical trials and may be in clinical settings.

A Simple Method for the Prediction of Human Concentration-Time Profiles and Pharmacokinetics of Antibody-Drug Conjugates (ADC) from Rats or Monkeys.

Knowledge of human concentration-time profiles from animal data can be useful during early drug development. The objective of this study is to predict human concentration-time profiles of antibody-drug conjugates (ADCs) and subsequently predict pharmacokinetic parameters in humans from rats or monkeys. Eight methods with different exponents of volume of distribution (0.8-1) as well as exponents of clearance (0.85), along with the exponents of volume of distribution for 5 ADCs, were used to predict human concentration-time profiles. The PK parameters were also scaled to humans from monkeys or rats using fixed exponents and compared with the PK parameters predicted from predicted human concentration-time profiles. The results of the study indicated that the exponent 0.9 and the combination of exponents of 0.9 and 0.8 (two exponents, 0.8 and 0.9, were used) were the best method to predict human concentration-time profiles and, subsequently, human PK parameters. The predicted PK parameters from fixed exponents were comparable with the predicted PK parameters estimated from human concentration-time profiles. The proposed methods are applicable to rats or monkeys with the same degree of accuracy. Overall, the proposed methods are robust, accurate, and cost- and time-effective.

Effect of Intrinsic and Extrinsic Factors on the Pharmacokinetics of Antibody-Drug Conjugates (ADCs).

Antibody-drug conjugates (ADCs) are complex molecules wherein a monoclonal antibody is linked to a biologically active drug (a small molecule), forming a conjugate. Initially, most of the ADCs were developed and are being developed for the treatment of cancer; however, with time, it has been realized that ADCs can also be developed to manage or cure other diseases. Pharmacokinetics (PK) plays an important role in modern-day drug development and the knowledge of PK is crucial in designing a safe and efficacious dose to treat a wide variety of diseases. There are several factors that can alter the PK of a drug; as a result, one has to adjust the dose in a patient population. These factors can be termed 'intrinsic' or 'extrinsic'. For small molecules, the impact of both intrinsic and extrinsic factors is well established. The impact of age, gender, disease states such as renal and hepatic impairment, drug-drug interaction, food, and in many cases alcohol on the PK of small molecules are well known. On the other hand, for macromolecules, the impact of these factors is not well established. Since the ADCs are a combination product of a monoclonal antibody linked to a small molecule, both the small molecule and the monoclonal antibody of the ADCs may be subjected to many intrinsic and extrinsic factors. This review summarizes the impact of intrinsic and extrinsic factors on the PK of ADCs and the payloads.

A Single Animal Species-Based Prediction of Human Clearance and First-in-Human Dose of Monoclonal Antibodies: Beyond Monkey.

These days, there is a lot of emphasis on the prediction of human clearance (CL) from a single species for monoclonal antibodies (mabs). Many studies indicate that monkey is the most suitable species for the prediction of human clearance for mabs. However, it is not well established if rodents (mouse or rat) can also be used to predict human CL for mabs. The objectives of this study were to predict and compare human CL as well as first-in-human dose of mabs from mouse or rat, ormonkey. Four methods were used for the prediction of human CL of mabs. These methods were: use of four allometric exponents (0.75, 0.80, 0.85, and 0.90), a minimal physiologically based pharmacokinetics method (mPBPK), lymph flow rate, and liver blood flow rate. Based on the predicted CL, first-in-human dose of mabs was projected using either exponent 1.0 (linear scaling) or exponent 0.85, and human-equivalent dose (HED) from each of these species. The results of the study indicated that rat or mouse could provide a reasonably accurate prediction of human CL as well as first-in-human dose of mabs. When exponent 0.85 was used for CL prediction, there were 78%, 95%, and 92% observations within a 2-fold prediction error for mouse, rat, and monkey, respectively. Predicted human dose fell within the observed human dose range (administered to humans) for 10 out of 13 mabs for mouse, 11 out of 12 mabs for rat, and 12 out of 15 mabs for monkey. Overall, the clearance and first-in-human dose of mabs were predicted reasonably well by all three species (a single species). On average, monkey may be the best species for the prediction of human clearance and human dose but mouse or rat especially; rat can be a very useful species for conducting the aforementioned studies.

Clinical Pharmacology of Antibody-Drug Conjugates.

Antibody-drug conjugates (ADCs) are biopharmaceutical products where a monoclonal antibody is linked to a biologically active drug (a small molecule) forming a conjugate. Since the approval of first ADC (Gemtuzumab ozogamicin (trade name: Mylotarg)) for the treatment of CD33-positive acute myelogenous leukemia, several ADCs have been developed for the treatment of cancer. The goal of an ADC as a cancer agent is to release the cytotoxic drug to kill the tumor cells without harming the normal or healthy cells. With time, it is being realized that ADCS can also be used to manage or cure other diseases such as inflammatory diseases, atherosclerosis, and bacteremia and some research in this direction is ongoing. The focus of this review is on the clinical pharmacology aspects of ADC development. From the selection of an appropriate antibody to the finished product, the entire process of the development of an ADC is a difficult and challenging task. Clinical pharmacology is one of the most important tools of drug development since this tool helps in finding the optimum dose of a product, thus preserving the safety and efficacy of the product in a patient population. Unlike other small or large molecules where only one moiety and/or metabolite(s) is generally measured for the pharmacokinetic profiling, there are several moieties that need to be measured for characterizing the PK profiles of an ADC. Therefore, knowledge and understanding of clinical pharmacology of ADCs is vital for the selection of a safe and efficacious dose in a patient population.

Spreadsheet-Based Minimal Physiological Models for the Prediction of Clearance of Therapeutic Proteins in Pediatric Patients.

There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.

Interspecies Scaling of Antibody-Drug Conjugates (ADC) for the Prediction of Human Clearance.

Allometric scaling is a useful tool for the extrapolation of pharmacokinetic parameters from animals to humans. The objective of this study was to predict human clearance of antibody-drug conjugates (ADC) allometrically from one to three animal species and compare the predicted human clearance with the observed human clearance. For three animal species allometric scaling, the "Rule of Exponents" (ROE) was used. The results of the study indicated that three-species allometric scaling in association with the ROE provides acceptable prediction (within 0.5-2-fold prediction error) of human clearance. The two-species allometric scaling resulted in substantial prediction error. One-species scaling using a fixed exponent of 1.0 provided acceptable prediction error (within 0.5-2-fold) by monkey, rat, and mouse, in which monkey and rat were comparable. Overall, the predicted human clearance values of ADCs from animal(s) was good. The allometric method proposed in this article can be used to predict human clearance from the animal data and subsequently to select the first-in-human dose of ADCs.

Impact of Intrinsic and Extrinsic Factors on the Pharmacokinetics of Peptides: When Is the Assessment of Certain Factors Warranted?

Peptides are short chains of 2 to 50 amino acids (molecular weight of less than 10 kDa) linked together by peptide bonds. As therapeutic agents, peptides are of interest because the body naturally produces many different peptides. Short-chain peptides have many advantages as compared with long-chain peptides (e.g., low toxicity). The first peptide corticotropin was approved in 1952 for multiple inflammatory diseases and West syndrome. Since then, more than 60 peptides have been approved by the FDA. Pharmacokinetics (PK) is widely used in modern-day drug development for designing a safe and efficacious dose to treat a wide variety of diseases. There are, however, several factors termed as "intrinsic" or "extrinsic" which can influence the PK of a drug, and as a result, one has to adjust the dose in a patient population. These intrinsic and extrinsic factors can be described as age, gender, disease states such as renal and hepatic impairment, drug-drug interaction, food, smoking, and alcohol consumption. It is well known that these intrinsic and extrinsic factors can have a substantial impact on the PK of small molecules, but for macromolecules, the impact of these factors is not well established. This review summarizes the impact of intrinsic and extrinsic factors on the PK of peptides.

Dosing Considerations for Antibodies Against COVID-19.

At present, no cure is available for COVID-19 but vaccines, antiviral drugs, immunoglobulins, or the combination of immunoglobulins with antiviral drugs have been suggested and are in clinical trials. The purpose of this paper is to discuss the role of a pharmacokinetic and viral load analysis as a basis for adjusting immunoglobulin dosing to treat COVID-19. We reviewed the pre-clinical and clinical literature that describes the impact of a high antigen load on pharmacokinetic data following antibody treatment. Representative examples are provided to illustrate the effect of high viral and tumor loads on antibody clearance. We then highlight the implications of these factors for facilitating the development and dosing of hyperimmune anti-SARS CoV2 immunoglobulin. Both nonclinical and clinical examples indicate that high antigen loads, whether they be viral, bacterial, or tumoral in origin, result in increased clearance and decreased area under the curve and half-life of antibodies. A dosing strategy that matches the antigen load can be achieved by giving initially high doses and adjusting the frequency of dosing intervals based on pharmacokinetic parameters. We suggest that study design and dose selection for immunoglobulin products for the treatment of COVID-19 require special considerations such as viral load, antibody-virus interaction, and dosing adjustment based on the pharmacokinetics of the antibody.

A GFR-Based Method to Predict the Effect of Renal Impairment on the Exposure or Clearance of Renally Excreted Drugs: A Comparative Study Between a Simple GFR Method and a Physiologically Based Pharmacokinetic Model.

OBJECTIVE: The objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment. METHODS: From the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CLT) = CLT in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error. RESULTS: There were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively. CONCLUSIONS: This study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.

Prediction of Clearance of Monoclonal and Polyclonal Antibodies and Non-Antibody Proteins in Children: Application of Allometric Scaling.

Allometric scaling can be used for the extrapolation of pharmacokinetic parameters from adults to children. The objective of this study was to predict clearance of therapeutic proteins (monoclonal and polyclonal antibodies and non-antibody proteins) allometrically in preterm neonates to adolescents. There were 13 monoclonal antibodies, seven polyclonal antibodies, and nine therapeutic proteins (non-antibodies) in the study. The clearance of therapeutic proteins was predicted using the age dependent exponents (ADE) model and then compared with the observed clearance values. There were in total 29 therapeutic proteins in this study with 75 observations. The number of observations with ≤30%, ≤50%, and >50% prediction error was 60 (80%), 72 (96%), and 3 (4%), respectively. Overall, the predicted clearance values of therapeutic proteins in children was good. The allometric method proposed in this manuscript can be used to select first-in-pediatric dose of therapeutic proteins in pediatric clinical trials.

Model-Based Evaluation of Linear Limited and Bayesian Sparse Sampling for Therapeutic Monitoring of Recombinant Coagulation Factor IX.

Dosing of coagulation factor products is mainly determined based on a patient's body weight; however, several studies have reported high interindividual variability in their pharmacokinetics (PK). The objective of this study was to develop and evaluate 2 sparse sampling methods for the estimation of AUC of recombinant factor IX (BeneFIX) as proof of concept for dose individualization. A population pharmacokinetic model was used to generate the plasma factor IX activity-versus-time data. The linear limited sampling model (LLSM) was developed based on the correlation of factor IX activity versus AUC0-72 hours following screening of several blood sampling times in adolescent and adult subjects (n = 90 subjects). Factor IX trough concentrations were predicted from a relationship established from AUC versus factor IX activity measured 72 hours postdosing. Using the best selected sampling time, the LLSM and Bayesian model were validated in separate data sets (n = 75 subjects). Using the LLSM and Bayesian analysis, a blood sample at 24 hours predicted AUC with bias and root mean square error < 5% and < 15%, respectively. The predicted trough concentrations were ≥1 IU/dL in 99% and 100% of subjects by the LLSM and Bayesian model, respectively. The average factor IX dose for a target AUC of 800 IU·h/dL was 61, 60, and 63 IU/kg using the extensive (reference), LLSM and Bayesian model, respectively. Overall, the AUC, trough concentrations and individualized dosing of recombinant factor IX could be reasonably predicted using the LLSM and Bayesian model.

Considerations for Optimizing Dosing of Immunoglobulins Based on Pharmacokinetic Evidence.

Immunoglobulins (IGs) are widely used for the treatment of immunodeficiency syndromes and several autoimmune diseases. In neonates, IGs have been used for the treatment of alloimmune thrombocytopenia, in neonatal infections and in the rare cases of neonatal Kawasaki disease. This review aims to examine the various dosing regimens of IGs following intravenous (IV) and subcutaneous (SC) administration, pharmacokinetics (PK) of IGs, and the importance of trough values for the prevention of infections in patients with primary immune deficiency (PID). The review also focuses on the mechanism of catabolism of IGs and the impact on the half-life of IGs. Data and reviews were obtained from the literature and the FDA package inserts. The authors suggest that for dosing, the PK of IGs should be evaluated on the baseline-corrected concentrations since this approach provides an accurate estimate of half-life and clearance of IGs. We also suggest employing clearance as a primary PK parameter for dosing determination of IGs. We suggest that IV dosing would be more effective if given more frequently to adjust for the increased clearance at high doses and because the baseline-corrected half-life is much shorter than the baseline-uncorrected half-life. Regarding SC administration, the dose should be adjusted based on the absolute bioavailability (determined against IV dosing) of the product. Finally, we highlight clinical and PK data gaps for optimum and individualized dosing of IGs.

Considerations for pharmacokinetic assessment of immunoglobulins: Gammagard in very low birth weight neonates with and without baseline-correction.

BACKGROUND: Immunoglobulins are widely used across multiple therapeutic areas such as immunodeficiency syndromes, infection and autoimmune diseases. The pharmacokinetics (PK) of immunoglobulins are well characterized in adults, but very little is known about the PK of immunoglobulins in neonates and infants. OBJECTIVE: The objective of the present study was to characterize the PK of Gammagard, an immunoglobulin, in very low birth weight preterm neonates. METHOD: Gammagard concentration-time data from very low birth weight neonates (bodyweight range 0.78-1.38 kg, n = 20) following intravenous administration of 500 mg/kg and 750 mg/kg were obtained from the literature. The data were analyzed with and without baseline correction using extensive blood samples (8 blood samples). Model-independent (non-compartmental) analysis was used to characterize the PK of Gammagard. RESULTS: Based on uncorrected baseline concentration-time data, the clearance and half-life of Gammagard were 3.1 ± 0.7 mL/day and 22 ± 6 days, respectively. Based on corrected baseline concentration-time data, the clearance and half-life of Gammagard were 20.2 ± 7.4 mL/day and 5.3 ± 2.2 days, respectively. CONCLUSION: The dose of immunoglobulins should be adjusted based on the PK of baseline corrected rather than baseline uncorrected profiles because baseline corrected PK parameters especially half-life reconciles with PK principles.

Prediction of Clearance in Children from Adults Following Drug-Drug Interaction Studies: Application of Age-Dependent Exponent Model.

BACKGROUND AND OBJECTIVE: Pharmacokinetic drug-drug interaction (DDI) studies are conducted in adult subjects during drug development but there are limited studies that have characterized pharmacokinetic DDI studies in children. The objective of this study was to evaluate if the DDI clearance values from adults can be allometrically extrapolated from adults to children. METHODS: Fifteen drugs were included in this study and the age of the children ranged from premature neonates to adolescents (30 observations across the age groups). The age-dependent exponent (ADE) model was used to predict the clearance of drugs in children from adults following DDI studies. RESULTS: The prediction error of drug clearances following DDIs in children ranged from 4 to 67%. Of 30 observations, 17 (57%) and 27 (90%) observations had a prediction error ≤ 30% and ≤ 50%, respectively. CONCLUSION: This study indicates that it is possible to predict the clearance of drugs with reasonable accuracy in children from adults following DDI studies using an ADE model. The method is simple, robust, and reliable and can replace other complex empirical models.