Transforming cancer drug discovery with Big Data and AI

Harnessing machine learning for rational drug design

A crucial part of biomedical research is drug discovery, which aims to find and create innovative medical treatments for a range of illnesses. However, there are intrinsic obstacles to the traditional approach of discovering novel medications, including high prices, lengthy turnaround times, and poor clinical trial success rates. In recent times, the use of designing algorithms for machine learning has become a groundbreaking way to improve and optimise many stages of medication development. An outline of the quickly developing area of machine learning algorithms for drug discovery is given in this review, emphasising how revolutionary treatment development might be. To effectively get a novel medication into the market, modern medicinal development often involves many interconnected stages. The use of computational tools has become more and more crucial in reducing the time and cost involved in the investigation and creation of new medications. Our latest efforts to combine molecular modelling as well as machine learning to create the computational resources for designing modulators utilising a sensible design influenced by the pocket process that targets protein-protein interactions via AlphaSpace are reviewed in this Perspective. A significant shift in pharmaceutical research has occurred with the introduction of AI in drug discovery, which combines cutting-edge computer techniques with conventional scientific investigation to address enduring problems. By highlighting significant advancements and methodologies, this review paper elucidates the many applications of AI throughout several stages of drug discovery.

Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy

Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, and durable treatment, making it effective in cancers resistant to conventional therapies. Advances in artificial intelligence (AI) present opportunities to enhance RIT by improving precision, efficiency, and personalization. AI plays a critical role in patient selection, treatment planning, dosimetry, and response assessment, while also contributing to drug design and tumor classification. This review explores the integration of AI into RIT, emphasizing its potential to optimize the entire treatment process and advance personalized cancer care.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

In silico ADMET profiling of Docetaxel and development of camel milk derived liposomes nanocarriers for sustained release of Docetaxel in triple negative breast cancer

This study aimed at encapsulation of commonly administered, highly cytotoxic anticancer drug Docetaxel (DTX) in camel milk fat globule-derived liposomes for delivery in triple negative breast cancer cells. Prior to liposomal encapsulation of drug, in silico analysis of Docetaxel was done to predict off target binding associated toxicities in different organs. For this purpose, the ADMET Predictor (TM) Cloud version 10.4.0.5, 64-bit, was utilized to simulate Docetaxel's pharmacokinetic and physicochemical parameters. Freshly milked camel milk was bought from local market, from two breeds Brella and Marecha, in suburbs of Islamabad. After extraction of MFGM-derived liposomes from camel milk, docetaxel was loaded into liposomes by thin film hydration method. The physiochemical properties of liposomes were analyzed by SEM, FTIR and Zeta analysis. The results from SEM showed that empty liposomes (Lp-CM-ChT80) had spherical morphology while DTX loaded liposomes (Lp-CM-ChT80-DTX) exhibited rectangular shape, FTIR revealed the presence of characteristic functional groups which confirmed the successful encapsulation of DTX. Zeta analysis showed that Lp-CM-ChT80-DTX had size of 836.6 nm with PDI of 0.088 and zeta potential of - 18.7 mV. The encapsulation efficiency of Lp-CM-ChT80 turned out to be 25% while in vitro release assay showed slow release of DTX from liposomes as compared to pure DTX using dialysis membrane. The in vitro anticancer activity was analyzed by cell morphology analysis and MTT cytotoxicity assay using different concentrations 80 µg/ml, 120 µg/ml and 180 µg/ml of Lp-CM-ChT80-DTX on MDA-MB-231 cells. The results showed cytotoxic effects increased in time and dose dependent manner, marked by rounding, shrinkage and aggregation of cells. MTT cytotoxicity assay showed that empty liposomes Lp-CM-ChT80 did not have cytotoxic effect while Lp-CM-ChT80-DTX showed highest cytotoxic potential of 60.2% at 180 µg/ml. Stability analysis showed that liposomes were stable till 24 h in solution form at 4 °C.

Chemical Probes Review: Choosing the Right Path Towards Pharmacological Targets in Drug Discovery, Challenges and Future Perspectives

Chemical probes are essential for academic research and target validation for disease identification. They facilitate drug discovery, target function investigation, and translation studies. A chemical probe provides starting material that can accelerate therapeutic values and safety measures for identifying any biological target in drug discovery. Essential read outs depend on their versatility in biochemical testing, proving the hypothesis, selectivity, specificity, affinity towards the target site, and valuable in new therapeutic approaches. Disease management will depend upon chemical probes as a primitive tool to ascertain the physicochemical stability for in vivo and in vitro studies useful for clinical trials and industrial application in the future. For cancer research, bacterial infection, and neurodegenerative disorders, chemical probes are integrated circuits which are on pipeline for the drug discovery process Furthermore, pharmacological modulators incorporate activators, crosslinkers, degraders, and inhibitors. Reports accessed depend on their structural, mechanical, biochemical, and pharmacological characterization in drug discovery research. The perspective for designing any chemical probes concludes with the utilization of drug discovery and identification of the potential target. It focuses mainly on evidence-based studies and produces promising results in successfully delivering novel therapeutics to treat cancers and other disorders at the target site. Moreover, natural product pharmacophores like rapamycin, cephalosporin, and α-lactamase are utilized for drug discovery. Chemical probes revolutionize computational-based study design depending on identifying novel targets within the database framework. Chemical probes are the clinical answers for drug development and goforward tools in solving other riddles for scientists and researchers working in this industries.

Repurposing Vandetanib plus Everolimus for the Treatment of ACVR1-Mutant Diffuse Intrinsic Pontine Glioma

Somatic mutations in ACVR1 are found in a quarter of children with diffuse intrinsic pontine glioma (DIPG), but there are no ACVR1 inhibitors licensed for the disease. Using an artificial intelligence-based platform to search for approved compounds for ACVR1-mutant DIPG, the combination of vandetanib and everolimus was identified as a possible therapeutic approach. Vandetanib, an inhibitor of VEGFR/RET/EGFR, was found to target ACVR1 (K d = 150 nmol/L) and reduce DIPG cell viability in vitro but has limited ability to cross the blood-brain barrier. In addition to mTOR, everolimus inhibited ABCG2 (BCRP) and ABCB1 (P-gp) transporters and was synergistic in DIPG cells when combined with vandetanib in vitro. This combination was well tolerated in vivo and significantly extended survival and reduced tumor burden in an orthotopic ACVR1-mutant patient-derived DIPG xenograft model. Four patients with ACVR1-mutant DIPG were treated with vandetanib plus an mTOR inhibitor, informing the dosing and toxicity profile of this combination for future clinical studies. SIGNIFICANCE: Twenty-five percent of patients with the incurable brainstem tumor DIPG harbor somatic activating mutations in ACVR1, but there are no approved drugs targeting the receptor. Using artificial intelligence, we identify and validate, both experimentally and clinically, the novel combination of vandetanib and everolimus in these children based on both signaling and pharmacokinetic synergies.This article is highlighted in the In This Issue feature, p. 275.

A model for an undergraduate research experience program in quantitative sciences

We developed a summer research experience program within a freestanding comprehensive cancer center to cultivate undergraduate students with an interest in and an aptitude for quantitative sciences focused on oncology. This unconventional location for an undergraduate program is an ideal setting for interdisciplinary training in the intersection of oncology, statistics, and epidemiology. This paper describes the development and implementation of a hands-on research experience program in this unique environment. Core components of the program include faculty-mentored projects, instructional programs to improve research skills and domain knowledge, and professional development activities. We discuss key considerations such as effective partnership between research and administrative units, recruiting students, and identifying faculty mentors with quantitative projects. We describe evaluation approaches and discuss post-program outcomes and lessons learned. In its initial two years, the program successfully improved students' perception of competence gained in research skills and statistical knowledge across several knowledge domains. The majority of students also went on to pursue graduate degrees in a quantitative field or work in oncology-centric academic research roles. Our research-based training model can be adapted by a variety of organizations motivated to develop a summer research experience program in quantitative sciences for undergraduate students.

Artificial Intelligence and Cancer Drug Development

BACKGROUND

The development of cancer drugs is among the most focused "bench to bedside activities" to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and Artificial Intelligence (AI). In the meantime, challenges, like curating the data of low quality, remain to be resolved.

OBJECTIVES

This review focused on the recent advancements and challenges of AI in developing cancer drugs.

METHODS

We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies.

RESULTS AND CONCLUSION

AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.

Artificial intelligence in clinical research of cancers

Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20 years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.

AI delivers Michaelis constants as fuel for genome-scale metabolic models

Michaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism.

Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics

Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein's function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins.

Public resources for chemical probes: the journey so far and the road ahead

High-quality small molecule chemical probes are extremely valuable for biological research and target validation. However, frequent use of flawed small-molecule inhibitors produces misleading results and diminishes the robustness of biomedical research. Several public resources are available to facilitate assessment and selection of better chemical probes for specific protein targets. Here, we review chemical probe resources, discuss their current strengths and limitations, and make recommendations for further improvements. Expert review resources provide in-depth analysis but currently cover only a limited portion of the liganded proteome. Computational resources encompass more proteins and are regularly updated, but have limitations in data availability and curation. We show how biomedical scientists may use these resources to choose the best available chemical probes for their research.

Derivation and Validation of a Prognostic Model for Cancer Dependency Genes Based on CRISPR-Cas9 in Gastric Adenocarcinoma

As a CRISPR-Cas9-based tool to help scientists to investigate gene functions, Cancer Dependency Map genes (CDMs) include an enormous series of loss-of-function screens based on genome-scale RNAi. These genes participate in regulating survival and growth of tumor cells, which suggests their potential as novel therapeutic targets for malignant tumors. By far, studies on the roles of CDMs in gastric adenocarcinoma (GA) are scarce and only a small fraction of CDMs have been investigated. In the present study, datasets of the differentially expressed genes (DEGs) were extracted from the TCGA-based (The Cancer Genome Atlas) GEPIA database, from which differentially expressed CDMs were determined. Functions and prognostic significance of these verified CDMs were evaluated using a series of bioinformatics methods. In all, 246 differentially expressed CDMs were determined, with 147 upregulated and 99 downregulated. Ten CDMs (ALG8, ATRIP, CCT6A, CFDP1, CINP, MED18, METTL1, ORC1, TANGO6, and PWP2) were identified to be prognosis-related and subsequently a prognosis model based on these ten CDMs was constructed. In comparison with that of patients with low risk in TCGA training, testing and GSE84437 cohort, overall survival (OS) of patients with high risk was significantly worse. It was then subsequently demonstrated that for this prognostic model, area under the ROC (receiver operating characteristic) curve was 0.771 and 0.697 for TCGA training and testing cohort respectively, justifying its reliability in predicting survival of GA patients. With the ten identified CDMs, we then constructed a nomogram to generate a clinically practical model. The regulatory networks and functions of the ten CDMs were then explored, the results of which demonstrated that as the gene significantly associated with survival of GA patients and Hazard ratio (HR), PWP2 promoted in-vitro invasion and migration of GA cell lines through the EMT signaling pathway. Therefore, in conclusion, the present study might help understand the prognostic significance and molecular functions of CDMs in GA.

Circulating cell-free DNA: Translating prostate cancer genomics into clinical care

Only in the past decade tremendous advances have been made in understanding prostate cancer genomics and consequently in applying new treatment strategies. As options regarding treatments are increasing so are the challenges in selecting the right treatment option for each patient and not the least, understanding the optimal time-point and sequence of applying available treatments. Critically, without reliable methods that enable sequential monitoring of evolving genotypes in individual patients, we will never reach effective personalised driven treatment approaches. This review focuses on the clinical implications of prostate cancer genomics and the potential of cfDNA in facilitating treatment management.