Machine learning strategies for identifying repurposed drugs for cancer therapy

Introduction

The 2018 Nobel Prize in Physiology or Medicine was awarded to two researchers for their discovery of immune checkpoint proteins [ ]. Although the discovery initiated the development of the revolutionary targeted immunotherapies, critical challenges remain to be addressed, making them far from perfection [ ]. Limitations have been observed earlier in chemotherapies, where cytotoxic molecular compounds are administered to directly kill tumor cells and slow the progression of disease [ ]. Other types of targeted therapies, where small molecules interfere with tumor-specific molecular abnormalities also has been actively studied for over half a century and used in parallel with other types of anticancer therapies. While advancements in our understanding of cancer biology and the treatment options made some types of cancers manageable, challenges remain in all types of anticancer therapies. First of all, the efficacy is often limited to a small subset of patients, and it is a difficult task to predict patients' responses to treatments [ ]. Furthermore, it is now well known that many anticancer drugs, including both immunotherapeutic and other targeted therapeutic agents, cause serious side effects [ ]. Natural redundancy and diversity in biological network, such as feedback mechanisms, which enhance robustness of phenotypical outcomes against perturbations, makes it difficult to pinpoint the targets that effectively stop tumor progression, and multidrug resistance develops over time, making the tumors immortal against the previously effective treatments [ ]. More effective and safer drugs are needed to battle against and eventually conquer cancers.

Despite the urgent need, drug discovery is generally a lengthy and costly process with high risk of failure. A variety of risk factors delay new drugs entering the market and increases the chance of withdrawal, increasing the overall cost of drug development [ ]. Drug-induced side effects and toxicity are one of the key issues relevant to the high rate of drug attrition [ ]. The limited success of drug development suggests flaws in the long-standing paradigm: one drug–one target–one disease, where the goal is to design a molecule that inhibits a biological target (e.g., protein) known to be crucial for the disease. Under the paradigm, drug candidate molecules are optimized to interact with the intended target (on-target), without proactively understanding off-target interactions and leaving safety and toxicity tests to the later stage of pipeline. Off-target interactions, most of the time unexpected, may cause undesirable outcomes, even fatal ones in extreme cases, leaving irreparable damages to the business and patients [ ]. On the other hand, the treatment of complex diseases may benefit from off-target interactions if both on- and off-target synergistically reverse the pathological processes. Indeed, it is now known that many approved drugs interact with more than one biological targets [ , ], and the importance of understanding the complex biological interactions for such multitargeting drug molecules is emphasized as a new paradigm of drug discovery, polypharmacology [ , ].

Instead of suffering from unexpected outcomes caused by off-target interactions, polypharmacology is an attempt to understand drug actions as results of multiple different interactions involving the drug molecule and maximize the benefit from the interactions. Indeed, the superiority of multitargeting drugs over highly selective single-target drugs is suggested [ ]. Under the new paradigm, off-target interactions of existing drugs can be used to repurpose them for new indications. Protein–ligand interaction profiles for new ligands can be computationally predicted, and the chemical scaffolds active for multiple targets of interest can be integrated into a single molecule to maximize therapeutic effects and minimize adverse events [ ]. New discoveries in biological studies reveal previously unattended anticancer targets, and new drugs can be designed to play multiple roles in the biological network [ , ]. Therefore, early understanding of drug–target interaction profile across whole genome space is essential for development of new, more effective and safer drugs. However, our knowledge of intermolecular interactions drug molecules cause is limited. It is prohibitive to experimentally evaluate all possible drug interactions. Drugs and drug candidate molecules are typically screened against a subset of potential biological targets, resulting in biased, noisy, sparse, and incomplete interaction profiles. At present, no experimental techniques are affordable and scalable enough to experimentally screen compounds for their complete bioactivity profile in humans.

Although not scalable yet to the whole human genome, high-throughput experimental methods have produced tremendous amount of compound bioactivity data, providing rich resources for data-driven knowledge discovery. To rapidly and systematically explore the data and discover hidden knowledge, various types of computational methods have been developed and applied to predict potential interactions of drug molecules. Among many classes of computational methods applicable for drug discovery, those specifically designed to predict unknown protein–ligand interactions are particularly suitable to fill in the sparse knowledge in bioactivity. This review aims to discuss the recent advancements in machine learning methods for the prediction of protein–ligand interactions as well as the efforts to collect and curate experimental data. While some methods directly aim to discover new anticancer therapies, other methods also provide opportunities to discover anticancer therapies when appropriate data sets and validation steps are incorporated. We try to guide readers who are interested in computer-aided drug discovery by providing information about collecting data, preprocessing the data, and methods that can take the processed data for inference. We discuss the major sources of biopharmaceutical databases that are frequently used to train and evaluate computational methods. Then, different types of computational approaches to predict intermolecular interactions are discussed with examples. Fig. 3.1 illustrates a general workflow for computational protein–ligand interaction prediction projects.

It should be emphasized that drug action is a complex process. The genome-wide protein–ligand interaction alone may be insufficient to predict clinical end points, such as therapeutic efficacies and side effects. A systems biology approach is needed to model the collective behavior of biomolecular interactions (e.g., DNA, RNAD, protein, metabolite, drug, etc.), which is beyond the scope of this chapter. Interested readers are referred to other publications [ ].

Open-access databases for computational drug discovery projects

High-quality and large-scale compound activity data (e.g., protein–ligand interactions) are indispensable in artificial intelligence–based drug discovery projects. To train a computational prediction method, often called a computational model, known protein–ligand interactions are provided in the way the model can accept. Many public databases curating experimentally measured protein–ligand binding affinities can be used for such purpose. The protein–ligand pairs are numerically represented using fingerprinting techniques described below, and the model is trained and evaluated on the numerically processed data sets. Therefore, it is important to appropriately choose and utilize the databases. Databases that provide compound bioactivity data or proteomic information are especially useful for protein–ligand interaction prediction projects. While they are roughly divided into bioactivity-centric and proteomic databases, many current databases are actively maintained and updated to integrate data from multiple resources, providing more rich and comprehensive data sets. Also, biological network data can be integrated into computational models to help better prediction. The following sections are to describe the commonly used bioactivity and proteomics databases for artificial intelligence–based drug discovery processes and the standard training and evaluation methods ( Table 3.1 ) . Other types of large-scale omics data include genomics, transcriptomics, metabolomics, and biological pathway information, and relevant databases are discussed elsewhere [ ].