What is Allometric Scaling and How Does it Support Drug Development?

Content updated on October 06, 2021

There are many aspects of drug development that must be understood before a drug can be tested in humans. Nonclinical studies are conducted to understand the safety (i.e., toxicity and safety pharmacology) of drug candidates and to provide critical pharmacokinetic (PK) information, including insights into the drug’s absorption, distribution, metabolism, and excretion (ADME) properties. Many drug candidates fail due to poor PK and/or safety profiles. Nonclinical studies provide actionable information that can be used for “go/no go” decisions early in the drug development process. This saves time, money, and potential harm to human subjects. Once the decision has been made to move into human studies, nonclinical data is used to predict human outcomes. One common method for predicting human doses is allometric scaling. In allometric scaling, PK data from nonclinical studies in one or more animal species are used to predict human drug exposure for a range of drug doses. This is a rapid method that can inform dosing decisions or determine if it is worthwhile to progress a particular therapeutic compound. In addition to predicting human exposure from nonclinical studies, allometric scaling can also be used when moving between species as the nonclinical program develops. It can also be used to predict drug doses for pediatric populations by using data from adults.

What is Allometric Scaling?

Allometric scaling is one of the tools that drug developers use to predict human PK based upon animal data. Prediction methods, like allometric scaling, provide a “sneak peek” at how a drug might behave in humans before any clinical studies are conducted. This is important information for both drug developers and regulators (like the FDA) because it provides a data-driven foundation for establishing a safe starting dose in humans. Allometric scaling comes from two words:

• ALLOMETRY: The study of size and its consequences
• SCALING: An engineering term meaning to adjust (or “scale”) dimensions (or other parameters) with size

In biology, the basis of allometric scaling lies in the relationship between metabolic rate (defined as the rate of biological life processes and metabolism) and the body size of the animal. The metabolic rate that is used in allometry includes life processes such as number of heart beats or number of breaths in the lifespan of the animal as a function of size. It is critical to understand that as body size increases from one animal species to another, metabolism slows down. To exploit this, allometric scaling uses mathematical models to describe physiological, anatomical, and biochemical changes in animals as their size changes. Of specific interest for drug development, this approach can predict important PK information for humans using experiments conducted in various animal species.

How is Allometric Scaling Used in Drug Development?

Allometric scaling is frequently used in drug development to inform strategies for first-in-human studies. This includes:

• Selecting a starting dose that will be both informative for the Sponsor and pose minimal risk for human subjects
• Predicting drug exposure and anticipated toxicity
• Designing a blood sample collection schedule to calculate PK and other parameters

For example, understanding expected maximum plasma concentration (Cmax), time of maximum concentration (Tmax), and half-life (t½) can inform how sensitive bioanalytical methods should be and how many blood samples need to be taken to get accurate drug concentration profiles in clinical studies.

Approaches for Predicting Human Exposures

There are several allometric methods that can be used to predict human drug exposure from nonclinical data.

Simply Allometry

In the simplest approach, PK data from one or more nonclinical studies are used to predict human drug exposure. PK parameters are scaled as a function of body mass using relatively straightforward techniques that do not require sophisticated mathematical models.

In Vitro/In Vivo Extrapolation (IVIVE)

IVIVE incorporates nonclinical data, as described above, with in vitro information, such as drug metabolism, plasma protein binding, permeability, solubility, or other relevant characteristics. This approach is more complicated but incorporates additional properties of the molecule and has advantages over simple allometry in certain situations.

Allometric Modeling and Simulation

In a modeling approach, a compartmental model is built with known PK, PD, and in vitro data and used to estimate PK parameters and how those parameters scale allometrically. Modeling can be conducted using a variety of software programs, such as Phoenix^® WinNonlin^® or NONlinear Mixed Effects Modeling (NONMEM). This model can also be used to run simulations with various doses to predict human exposure and exposure-response relationships. These methods can also predict variability in parameters and provide an estimate of how good the model is.

Human Equivalent Dose (HED) Calculation

HED is a calculation method used to determine the maximum safe starting dose in first in human clinical trials. These calculations use common conversion factors based on body surface area. For example, to convert a drug dose (in mg/kg) to a HED in mg/kg for a rabbit, the animal dose would either be divided by 3.1 or multiplied by 0.32. Details on estimating doses for first in human studies are detailed in the 2005 FDA Guidance. However, it is worth noting that HED calculations do not directly scale PK parameters, so there is no way to simulate concentration-time profiles, as can be done with allometric scaling approaches.

Physiologically Based Pharmacokinetic Modeling (PBPK)

PBPK analyses uses models and simulations that combine physiology, population, and drug characteristics to describe PK and/or PD behaviors of a drug. PBPK models differ from allometric scaling in that they require richer information and integrate data from a variety of sources, including data on distribution of drug into tissues, tissue mass, blood flow, etc. Factors that may differ between species, age, and disease state may also be incorporated. PBPK modeling is a common alternative to allometric scaling but requires substantially more data and model development than methods such as simple allometry.

Limitations of Allometric Scaling

Allometric scaling methods are not perfect, and there are some limitations. Simple allometric scaling (Approach #1, above) can be misleading when key differences between species are not considered. Of particular importance, differences in key metabolizing enzymes, transporters, or protein binding across species may lead to difficulty performing simple allometric scaling. In such cases, IVIVE or PBPK may be a better approach. In our experience, allometry tends to be work best for drugs that fall under the class of peptides or proteins, since the biological processes involved in drug metabolism tend to be evolutionarily conserved across species. It is important to note that there is always some uncertainty associated with allometric predictions.  These methods provide an important guide to initial dose selection or dose adjustment but are not a substitute for collecting relevant clinical data.

Conclusions

There are multiple methods that can be used to predict human exposure. Allometric scaling is an important method that leverages nonclinical results to inform critical decisions in clinical drug development. Predictions from allometric scaling allow go/no go decisions about drug candidates, support selection of safe first-in-human doses and allow human studies to be effectively designed to obtain safety and PK information. Allucent's experts are adept at using allometric scaling, IVIVE allometric scaling, and modeling and simulation to evaluate nonclinical data and design high quality first-in-human studies. Contact us to learn how Allucent can support your clinical development program by helping you to choose the best scaling and prediction approach for your compound.