A disease model is a biological system used by scientists to replicate aspects of a human disease outside of the patient’s body. These models mimic the pathological processes, symptoms, or genetic features of a specific condition within a controlled laboratory setting. Researchers rely on these systems because they allow the study of complex human ailments in a way that would be impossible or unethical to perform on people. The complexity of these models varies widely, ranging from simple, isolated cells grown in a dish to sophisticated, genetically modified living organisms.
Why Scientists Rely on Disease Models
Disease models offer an isolated and controlled environment for investigation, enabling experiments that are not feasible in human patients. This setting is fundamental to understanding the mechanisms by which a disease develops and progresses. By manipulating specific genes or molecular pathways in the model, researchers can identify which biological signals are driving the disease state.
Understanding the progression of a disease is a major application, often involving the search for molecular pathways that go awry. For instance, a model can be used to track the accumulation of a misfolded protein in a neurodegenerative disorder or observe the uncontrolled division of cells in a cancer model. This work helps pinpoint potential biological targets—specific proteins or genes—that, if corrected, could potentially halt or reverse the disease.
Disease models are also used for testing potential treatments, a process known as preclinical screening. Before any new compound is administered to human volunteers, it must be tested in disease models to assess its efficacy and safety. This screening allows researchers to rapidly evaluate thousands of drug candidates to determine which ones show promise and do not cause unacceptable levels of toxicity.
The Main Types of Disease Models
Models used in research are categorized into three types based on their physical nature. In vitro models, meaning “in glass,” are the simplest and involve studying disease processes using isolated cells or tissues in a dish. These include traditional two-dimensional (2D) cell cultures, and more advanced three-dimensional (3D) structures like spheroids and organoids that better mimic tissue architecture.
Organoids are tiny, self-organized 3D structures that can replicate the function and cellular arrangement of organs such as the brain, liver, or gut. Patient-derived organoids (PDOs) are grown from a patient’s own tissue, allowing researchers to test how a specific individual’s tumor will respond to various cancer drugs.
The organ-on-a-chip system is a newer approach that uses microfluidic technology. This system simulates the mechanical and biochemical environment of human organs, including blood flow and tissue-to-tissue interfaces.
In vivo models, meaning “in the living,” use whole organisms to study disease within a complete biological system. Rodents are frequently chosen due to their genetic similarity to humans and the relative ease of manipulating their genomes to introduce disease-causing mutations. Researchers can create genetically modified models that express human genes or lack specific genes to induce a condition that mirrors a human disorder.
The third category, computational models, relies on computer simulations and mathematical algorithms to predict biological behaviors from large datasets. These simulations use systems biology and empirical data to model complex interactions, such as how a virus infects host cells or how a drug is metabolized. Computational modeling is useful for identifying patterns in disease progression and predicting therapeutic approaches before costly laboratory experiments begin.
Ensuring Models Accurately Reflect Human Disease
A challenge in research is ensuring the disease model accurately reflects the human condition, a concept referred to as model validity or fidelity. Researchers must validate their models by checking if the model exhibits the same symptoms, genetic changes, and responses to known treatments as the human disease. Without this alignment, the results generated by the model may not be relevant to human biology.
The difficulty of translating findings from the lab to the clinic, particularly from animal models to human patients, is a hurdle in drug development. Many compounds that show efficacy in a mouse model ultimately fail in human clinical trials, a phenomenon often attributed to fundamental biological differences between species. For instance, the way a drug is absorbed, distributed, metabolized, and excreted—its pharmacokinetics—can vary significantly between a rodent and a human.
Complex human diseases, such as Alzheimer’s or diabetes, involve genetic, lifestyle, and environmental factors that a simplified model cannot capture. An animal model may only replicate one specific aspect of a disease, failing to account for the full spectrum of patient heterogeneity and comorbidities present in a human population. Researchers are increasingly focused on developing human-based models, like organoids and organ-on-a-chip systems, to improve the predictive power of preclinical research and bridge the translational gap to new therapies.