Intelli Pharmaceuticals
Karlotta Neifert
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The U.S. Food and Drug Administration (FDA) issued "Artificial Intelligence and Medical Products: How CBER, CDER, CDRH, and OCP are Working Together," which outlines the agency's commitment and cross-center collaboration to protect public health while fostering responsible and ethical medical product innovation through Artificial Intelligence.
Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The complex and dynamic processes involved in the development, deployment, use, and maintenance of AI technologies benefit from careful management throughout the medical product life cycle.
Artificial Intelligence is a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. Artificial intelligence systems use machine- and human-based inputs to perceive real and virtual environments; abstract such perceptions into models through analysis in an automated manner; and use model inference to formulate options for information or action.
AI/ML technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance.
The FDA reviews medical devices through an appropriate premarket pathway, such as premarket clearance (510(k)), De Novo classification, or premarket approval. The FDA may also review and clear modifications to medical devices, including software as a medical device, depending on the significance or risk posed to patients of that modification. Learn the current FDA guidance for risk-based approach for 510(k) software modifications.
The FDA's traditional paradigm of medical device regulation was not designed for adaptive artificial intelligence and machine learning technologies. Many changes to artificial intelligence and machine learning-driven devices may need a premarket review.
On April 2, 2019, the FDA published a discussion paper "Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback" that describes a potential approach to premarket review for artificial intelligence and machine learning-driven software modifications.
In January 2021, the FDA published the "Artificial Intelligence and Machine Learning Software as a Medical Device Action Plan" or "AI/ML SaMD Action Plan." Consistent with the action plan, the FDA later issued the following documents:
On March 15, 2024 the FDA published the "Artificial Intelligence and Medical Products: How CBER, CDER, CDRH, and OCP are Working Together," which represents the FDA's coordinated approach to AI. This paper is intended to complement the "AI/ML SaMD Action Plan" and represents a commitment between the FDA's Center for Biologics Evaluation and Research (CBER), the Center for Drug Evaluation and Research (CDER), and the Center for Devices and Radiological Health (CDRH), and the Office of Combination Products (OCP), to drive alignment and share learnings applicable to AI in medical products more broadly.
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Automated analysis of medical and chemical knowledge to extract and represent features in a human-intelligible format dates back to the 1990s26,27, but has been receiving increasing attention due to the re-emergence of neural networks in chemistry and healthcare. Given the current pace of AI in drug discovery and related fields, there will be an increased demand for methods that help us understand and interpret the underlying models. In an effort to mitigate the lack of interpretability of certain machine learning models, and to augment human reasoning and decision-making,28, attention has been drawn to explainable AI (XAI) approaches29,30.
In general, XAI-generated explanations can be categorized as global (that is, summarizing the relevance of input features in the model) or local (that is, based on individual predictions)36. Moreover, XAI can be dependent on the underlying model, or agnostic, which in turn affects the potential applicability of each method. In this framework, there is no one-fits-all XAI approach.
Drug design is not straightforward. It distinguishes itself from clear-cut engineering by the presence of error, nonlinearity and seemingly random events38. We have to concede our incomplete understanding of molecular pathology and our inability to formulate infallible mathematical models of drug action and corresponding explanations. In this context, XAI bears the potential to augment human intuition and skills for designing novel bioactive compounds with desired properties.
The field of XAI is still in its infancy but moving forward at a fast pace, and we expect an increase of its relevance in the years to come. In this Review, we aim to provide a comprehensive overview of recent XAI research, highlighting its benefits, limitations and future opportunities for drug discovery. In what follows, after providing an introduction to the most relevant XAI methods structured into conceptual categories, the existing and some of the potential applications to drug discovery are presented. Finally, we discuss the limitations of contemporary XAI and point to the potential methodological improvements needed to foster practical applicability of these techniques to pharmaceutical research.
Active learning. Field of machine learning in which an underlying model can query an oracle (for example, an expert or any other information source) in an active manner to label new data points with the goal of learning a task more efficiently.
Fragment-based virtual screening. Computational approach aimed to obtain promising hit or lead compounds based on the presence of specified molecular fragments (for example, molecular substructures known to possess or convey a certain desired biological activity).
Gaussian process. Supervised, Bayesian-inspired machine learning model that naturally handles uncertainty estimation over its predictions. It does so by inducing a prior over functions with a covariance function that measures similarity among the inputs. Gaussian process models are often used for solving regression tasks.
This section aims to provide a concise overview of modern XAI approaches, and exemplify their use in computer vision, natural-language processing and discrete mathematics. We then highlight selected case studies in drug discovery and propose potential future areas and research directions of XAI in drug discovery. A summary of the methodologies and their goals, along with reported applications is provided in Table 1. In what follows, without loss of generality, f will denote a model (in most cases a neural network); \(x \in \calX\) will be used to denote the set of features describing a given instance, which are used by f to make a prediction \(y \in \calY\).
Given a regression or classification model \(f:\boldsymbolx \in \Bbb R^K \to \Bbb R\) (where \(\Bbb R\) refers to the set of real numbers, and K (as a superscript of \(\Bbb R\)) refers to a k-dimensional set of real numbers), a feature attribution method is a function \(\calE:\boldsymbolx \in \Bbb R^K \to \Bbb R^K\) that takes the model input and produces an output whose values denote the relevance of every input feature for the final prediction computed with f. Feature attribution methods can be grouped into the following three categories (Fig. 1).
Gradient-based feature attribution. These approaches measure how much a change around a local neighbourhood of the input x corresponds to a change in the output f(x). A common approach among deep-learning practitioners relies on the use of the derivative of the output of the neural network with respect to the input (that is, δf/δx) to determine feature importance47,48. Its popularity arises partially from the fact that this computation can be performed via back-propagation49, the main way of computing partial first-order derivatives in neural network models. While the use of gradient-based feature attribution may seem straightforward, several methods relying on this principle have been shown to lead to only partial reconstruction of the original features50, which is prone to misinterpretation.
where \(z_i \in \left\ 0,1 \right\^M\), M is the number of original input features, \(\phi _i \in \Bbb R\) are coefficients representing the importance assigned to each ith binary variable and ϕ0 is an intercept. Several notable feature attribution methods belong to this family51,52, such as local interpretable model-agnostic explanations (LIME)53, Deep Learning Important FeaTures (DeepLIFT)54, Shapley additive explanations (SHAP)52 and layer-wise relevance propagation55. Both gradient-based methods and the additive subfamily of surrogate attribution methods provide local explanations (that is, each prediction needs to be examined individually), but they do not offer a general understanding of the underlying model f. Global surrogate explanation models aim to fill this gap by generically describing f via a decision tree or decision set56 model. If such an approximation is precise enough, these aim to to mirror the computation logic of the original model. While early attempts limited f to the family of tree-based ensemble methods (for example, random forests57), more recent approaches are readily applicable to arbitrary deep learning models58.
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