More Accurate Prediction of Human Pharmacokinetic Parameters for Small Molecule Compounds: Methods, Models, and Applications

New drug development is a complex, time-consuming, and challenging process, ultimately aimed at creating safe and effective therapeutics for humans. Before entering clinical trials, a series of preclinical studies is conducted to predict a drug's behavior in humans. Drug Metabolism and Pharmacokinetics (DMPK) is a critical discipline for evaluating a drug's absorption, distribution, metabolism, and excretion (ADME) in vivo. To enhance the translation of preclinical findings to clinical outcomes, human clinical pharmacokinetic (PK) characteristics can be predicted using in vitro and animal-derived PK data.

Accurate prediction of human PK properties is vital for drug development, clinical translation, and application. First, predicting absorption, distribution, and metabolism profiles aids in screening and optimizing candidate compounds, improving efficacy while mitigating adverse effects. Second, forecasting clinical PK properties guides the design of human dosing regimens, ensuring efficacy and safety during trials and treatment. Additionally, individual variability significantly impacts drug efficacy and safety; DMPK predictions can elucidate the sources of such variability, enabling personalized therapy and improved outcomes.

Methods for Predicting Human DMPK Parameters of Small Molecule Compounds

Advances in computational science and biotechnology have expanded the methods and models used in clinical PK prediction. Key approaches include:

• In Vitro-In Vivo Extrapolation (IVIVE) ^[1]

• Allometric scaling using animal data to predict human PK ^[2]

• Physiologically Based Pharmacokinetic (PBPK) modeling ^[2]

• Machine learning algorithms ^[3]

In Vitro-In Vivo Extrapolation (IVIVE) Prediction Methods

Cell Permeability and Transporter Assays for Human Absorption and Distribution Prediction

Cell models (e.g., Caco-2) assess drug permeability and transporter interactions to predict human absorption and distribution. For predicting oral PK of 18 drugs, Caco-2 apparent permeability (P_app) effectively estimated the absorption rate constant (k_a) using^[5]:

Where: 

P_app: Caco-2 apparent permeability;

S: Absorption surface area (200 m²);

V_c: Central volume of distribution (predicted).

Caco-2 P_app also predicts fraction absorbed (F_a) from intestine to enterocytes^[6]:

Where: 

P_eff (10^-4 cm/s): Human jejunal permeability (balloon endoscopy);

Tres: Small intestine transit time (3 h);

R: Small intestine radius (2 cm).

In Vitro Metabolic Stability Assays for In Vivo Metabolic Clearance Prediction

In vitro liver metabolism data (e.g., human liver microsomes or hepatocytes) predict in vivo metabolic clearance. Using the well-stirred model, intrinsic hepatic clearance (CL_int, liver) is scaled to human clearance (CL) (8):

Where: 

Q_h: Hepatic blood flow; 

f_u(b): Fraction unbound in blood; 

CL_int, liver: Intrinsic hepatic clearance; f_u(inc): Fraction unbound in microsomes/hepatocytes.

For details, see the article Predicting In Vivo Metabolic Clearance Using In Vitro–In Vivo Extrapolation (IVIVE) Model: Why and How?

Protein binding is critical in IVIVE-based CL prediction. Traditional IVIVE performs well for most drugs but has limitations for highly protein-bound, low-clearance, or transporter-interacted compounds.

Recent mechanistic IVIVE methods address these gaps by incorporating:

• Protein binding correction factors for free drug concentrations in hepatocytes.

• In vitro protein binding experiments (plasma, microsomes).

• pH-dependent adjustments (e.g., Berezhkovskiy’s equation for basic compounds).

• Ion interactions between protein-drug complexes and cell surfaces (Poulin’s model) ^[9].

Table 1. Formulas for Predicting Clearance from In Vitro Enzyme Metabolism Data

R_b/p: Blood-to-plasma ratio;

f_u(p)−app: Apparent fraction unbound (pH-adjusted);

f_u(liver): Liver fraction unbound (pH/protein-facilitated uptake).

These in vitro methods can provide preliminary predictions of a drug’s pharmacokinetic properties in humans, offering foundational data and guidance for further animal models and clinical research. However, it is important to note that in vitro methods can only provide initial predictions, and actual in vivo PK properties need to be verified and confirmed through animal models and clinical trials.

In Vivo Prediction Methods

Allometric Scaling for Human PK Parameter Prediction

Conduct pharmacokinetic experiments using animals such as mice, rats, dogs, and monkeys to evaluate the absorption, distribution, metabolism, and excretion of drugs in animals. Then, use allometric scaling to predict the PK characteristics in humans. Allometric scaling is based on anatomical, physiological, and biochemical similarities between preclinical species and humans, assuming that differences in pharmacokinetic characteristics among species mainly arise from differences in body weight and organ size. This is one of the earliest and most widely used methods for predicting human PK (Figure 1). Simple allometric scaling (SAS) is a basic allometric method that calculates parameters in the allometric equation using data from multiple species, then predicts data for other species. The formula for predicting clearance (CL) using simple allometric scaling is:

Figure 1. Predicting Human PK Parameters Using an Allometric Scaling Model  ^[10]

However, simple allometric scaling cannot accurately predict the clearance rate of all drugs. To improve prediction accuracy, various improved methods have been developed, including the Rule of Exponents (ROE) correcting maximum lifespan potential (MLP) and brain weight (BrW), the fu-corrected intercept method (FCIM), single-species scaling (SSS), and two-species scaling (TS) (Table 2). Comparative studies by the Pharmaceutical Research and Manufacturers of America (PhRMA) found that the methods with higher prediction accuracy are the free compound intercept method and the rat-dog two-species scaling method (TS_R-D)^[11].    

Table 2. Human Clearance (CL) Prediction Methods Based on Allometric Scaling  ^[12]

BrW: Brain weight; MLP: Maximum lifespan potential.

Steady-state apparent volume of distribution (V_ss) is an important parameter for measuring drug distribution in tissues. Allometric scaling is also applicable for predicting V_ss. Studies show that results vary little when using multiple or single species for allometric scaling. In the early discovery phase of new drugs, data from a single animal species can be used to predict human V_ss to improve screening efficiency (Table 3).

Table 3. Prediction Methods for Human Steady-State Apparent Volume of Distribution (V_ss) Based on Allometric Scaling ^[13]  

Øie-Tozer Method

The Øie-Tozer model is semi-mechanistic, a term used in pharmacokinetics and pharmacodynamics to describe a method that combines features of mechanistic and empirical models. The Øie-Tozer model is primarily used to predict the tissue-plasma partition ratio of drugs in the body. It is based on integrating preclinical animal species data, designed to describe drug behavior in different physiological environments through simple mathematical formulas. The model assumes that the unbound fraction in human tissues (f_ut) is equal to that in animal models, allowing researchers to infer human pharmacokinetic characteristics using existing animal experimental data.  ^[13]

Table 4.  Predicting V_ss Using the Øie-Tozer Model

Here, V_e is the extracellular fluid volume, V_p is plasma volume, V_r is residual fluid volume, RE/I is the ratio of binding protein in extracellular fluid to binding protein in plasma. V_p (L/kg) = 0.0313, 0.0313, 0.0515, 0.0448, and 0.0436 correspond to mouse, rat, dog, monkey, and human, respectively. V_e (L/kg) = 0.265, 0.265, 0.216, 0.208, and 0.151 correspond to mouse, rat, dog, monkey, and human, respectively. R_E/I = 1.4. V_r (L/kg) = 0.364, 0.364, 0.450, 0.485, and 0.380 correspond to mouse, rat, dog, monkey, and human, respectively.

PBPK Method for Predicting V_ss

Aside from the semi-mechanistic Øie-Tozer model, physiologically based pharmacokinetic (PBPK) models also perform well in predicting V_ss. PBPK is a mathematical model used to simulate and predict the absorption, distribution, metabolism, and excretion of drugs within a biological system. This method is based on physiological and anatomical data combined with the chemical properties of the drug, establishing models suitable for humans or animals. By integrating in vivo and in vitro data, PBPK models can provide more accurate predictions of a drug's pharmacokinetic properties in vivo (Table 5).   ^[14]

Table 5. Predict V_ss using the PBPK model

Here, V_p is plasma volume; V_e is red blood cell volume (V_blood-V_p); E/P is red blood cell-plasma ratio, calculated by hematocrit/(1- hematocrit); K_pi and V_i are tissue-plasma distribution ratio and volume for the i-th tissue, respectively.

Computational Simulations

With the advancement of artificial intelligence (AI), researchers have begun exploring the direct establishment of relationships between compound structures and pharmacokinetic parameters. Consequently, AI models have been developed and trained to predict clearance rates, distribution volumes, and other parameters from compound structures. For instance, using the ADMET Predictor^® software, one can input a compound's structure and use built-in AI models to predict human apparent distribution volume, intrinsic clearance in microsomes or hepatocytes, and more.  ^[16]

Prediction Case: Human Pharmacokinetic Parameter Prediction for MRTX-849

MRTX-849 (Krazati) is a KRAS G12C mutation inhibitor developed by Mirati Therapeutics, primarily for treating non-small cell lung cancer (NSCLC) patients with the KRAS G12C mutation. Here, we use various in vitro and in vivo methods to predict the human pharmacokinetic parameters of MRTX-849, comparing them with clinical PK data.

The prediction mainly employed quantitative systems pharmacology (QSP) combined with the GastroPlus^® PBPK model. Initially, allometric scaling was used to predict the clearance rate of MRTX-849 after intravenous administration in humans. Different methods were used to predict human clearance (CL) (see Table 6), showing that the TS_R,D method's predicted clearance was closest to the ROE method result.

Table 6. Predicted Clearance (CL) of MRTX-849 in Humans Using Different Methods

Different methods were also used to predict the human apparent distribution volume (V_ss), showing that the Øie-Tozer method’s predicted V_ss was closest to the GastroPlus^® software’s Lukacova formula result (see Table 7).

Table 7. Predicted Apparent Distribution Volume (V_ss) of MRTX-849 in Humans Using Different Methods

Summary: In constructing the PBPK model, clearance data from the ROE method and distribution volume data from the Lukacova formula were utilized, yielding accurate predictions of human intravenous PK parameters. Subsequently, the GastroPlus^® IV PBPK model and actual preclinical animal pharmacokinetic data were combined to build and optimize a prediction model, which was then used to predict the oral PK parameters of MRTX-849 in humans. Notably, the differences between predicted and measured values were within twofold, demonstrating the high accuracy and reliability of our methods (Figure 2).

Figure 2. Plasma Concentration-Time Curve of 200 mg Oral MRTX-849 Predicted Using Quantitative Systems Pharmacology (QSP) Combined with GastroPlus^® Software

Discussion and Conclusion

Human PK prediction for small molecules is indispensable in drug development and clinical applications. It streamlines candidate screening/optimization, reduces costs/failure rates, and guides clinical dosing (Figure 3). WuXi AppTec DMPK teams leverage computational models and experimental data to support human PK predictions across development stages, accelerating the advancement of candidates with favorable PK properties.

Figure 3. Pharmacokinetic models are used to predict human drug exposure based on preclinical data ^[17]

Authors: Yan Pan, Yunyi Yan, Liping Ma, Lijuan Hou, Jing Jin

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