De novo drug design through artificial intelligence: an introduction

Developing new drugs is a complex and formidable challenge, intensified by rapidly evolving global health needs. De novo drug design is a promising strategy to accelerate and refine this process. The recent introduction of Generative Artificial Intelligence (AI) algorithms has brought new attention to the field and catalyzed a paradigm shift, allowing rapid and semi-automatic design and optimization of drug-like molecules. This review explores the impact of de novo drug design, highlighting both traditional methodologies and the recently introduced generative algorithms, as well as the promising development of Active Learning (AL). It places special emphasis on their application in oncological drug development, where the need for novel therapeutic agents is urgent. The potential integration of these AI technologies with established computational and experimental methods heralds a new era in the rapid development of innovative drugs. Despite the promising developments and notable successes, these technologies are not without limitations, which require careful consideration and further advancement. This review, intended for professionals across related disciplines, provides a comprehensive introduction to AI-driven de novo drug design of small organic molecules. It aims to offer a clear understanding of the current state and future prospects of these innovative techniques in drug discovery

Identification of new putative driver mutations and predictors of disease evolution in chronic lymphocytic leukemia

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A Three-Gene Expression Signature Identifies a Cluster of Patients with Short Survival in Chronic Lymphocytic Leukemia.

Chronic lymphocytic leukemia (CLL) is a lymphoproliferative disorder characterized by its heterogeneous clinical evolution. Despite the discovery of the most frequent cytogenomic drivers of disease during the last decade, new efforts are needed in order to improve prognostication. In this study, we used gene expression data of CLL samples in order to discover novel transcriptomic patterns associated with patient survival. We observed that a 3-gene expression signature composed of SCGB2A1, KLF4, and PPP1R14B differentiate a group of circa 5% of cases with short survival. This effect was independent of the main cytogenetic markers of adverse prognosis. Finally, this finding was reproduced in an independent retrospective cohort. We believe that this small gene expression pattern will be useful for CLL prognostication and its association with CLL response to novel drugs should be explored in the future

New Recurrent Structural Aberrations in the Genome of Chronic Lymphocytic Leukemia Based on Exome-Sequencing Data

Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in Western countries, and it is characterized by recurrent large genomic rearrangements. During the last decades, array techniques have expanded our knowledge about CLL’s karyotypic aberrations. The advent of large sequencing databases expanded our knowledge cancer genomics to an unprecedented resolution and enabled the detection of small-scale structural aberrations in the cancer genome. In this study, we have performed exome-sequencing-based copy number aberration (CNA) and loss of heterozygosity (LOH) analysis in order to detect new recurrent structural aberrations. We describe 54 recurrent focal CNAs enriched in cancer-related pathways, and their association with gene expression and clinical evolution. Furthermore, we discovered recurrent large copy number neutral LOH events affecting key driver genes, and we recapitulate most of the large CNAs that characterize the CLL genome. These results provide “proof-of-concept” evidence supporting the existence of new genes involved in the pathogenesis of CLL.S

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes

Machine learning; Risk stratification; Myelodysplastic neoplasmsAprendizaje automático; Estratificación del riesgo; Neoplasias mielodisplásicasAprenentatge automàtic; Estratificació del risc; Neoplàsies mielodisplàstiquesMyelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis

Aprendizaje automático; MielofibrosisAprenentatge automàtic; MielofibrosiMachine learning; MyelofibrosisMyelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification

Variable Expressivity and Allelic Heterogeneity in Type 2 Familial Partial Lipodystrophy: The p.(Thr528Met) LMNA Variant

Type 2 familial partial lipodystrophy, or Dunnigan disease, is a metabolic disorder characterized by abnormal subcutaneous adipose tissue distribution. This rare condition results from variants principally affecting exons 8 and 11 of the LMNA gene. In this study, five FPLD2-diagnosed patients carrying the c.1583C>T, p.(Thr528Met) variant in exon 9 of the LMNA gene and with obvious clinical heterogeneity were evaluated. Specific polymorphisms in LMNA and in PPARG were also detected. Exhaustive clinical course, physical examination, biochemical features and family history were recorded, along with the assessment of anthropometric features and body composition by dual-energy X-ray absorptiometry. Preadipocytes obtained from a T528M patient were treated with the classic adipose differentiation medium with pioglitazone. Various adipogenes were evaluated by real-time PCR, and immunofluorescence was used to study intracellular localization of emerin, lamin A and its precursors. As demonstrated with Oil red O staining, the preadipocytes of the T528M patient failed to differentiate, the expression of various adipogenic genes was reduced in the lipodystrophic patient and immunofluorescence studies showed an accumulation of farnesylated prelamin A in T528M cells. We conclude that the T528M variant in LMNA could lead to FPLD2, as the adipogenic machinery is compromisedThis research was funded by the Instituto de Salud Carlos III and the European Regional Development Fund, FEDER (grant number PI081449), and an intramural grant from the Xunta de Galicia (grant number ED431B 2020/37). S.S.I. was awarded a Research Fellowship by the Asociación Española de Familiares y Afectados de Lipodistrofias (AELIP)S

Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns

Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in western countries. CLL evolution is frequently indolent, and treatment is mostly reserved for those patients with signs or symptoms of disease progression. In this work, we used RNA sequencing data from the International Cancer Genome Consortium CLL cohort to determine new gene expression patterns that correlate with clinical evolution.We determined that a 290-gene expression signature, in addition to immunoglobulin heavy chain variable region (IGHV) mutation status, stratifies patients into four groups with notably different time to first treatment. This finding was confirmed in an independent cohort. Similarly, we present a machine learning algorithm that predicts the need for treatment within the first 5 years following diagnosis using expression data from 2,198 genes. This predictor achieved 90% precision and 89% accuracy when classifying independent CLL cases. Our findings indicate that CLL progression risk largely correlates with particular transcriptomic patterns and paves the way for the identification of high-risk patients who might benefit from prompt therapy following diagnosis.S

The association of germline variants with chronic lymphocytic leukemia outcome suggests the implication of novel genes and pathways in clinical evolution

Background Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disorder in western countries and is characterized by a remarkable clinical heterogeneity. During the last decade, multiple genomic studies have identified a myriad of somatic events driving CLL proliferation and aggressivity. Nevertheless, and despite the mounting evidence of inherited risk for CLL development, the existence of germline variants associated with clinical outcomes has not been addressed in depth. Methods Exome sequencing data from control leukocytes of CLL patients involved in the International Cancer Genome Consortium (ICGC) was used for genotyping. Cox regression was used to detect variants associated with clinical outcomes. Gene and pathways level associations were also calculated. Results Single nucleotide polymorphisms in PPP4R2 and MAP3K4 were associated with earlier treatment need. A gene-level analysis evidenced a significant association of RIPK3 with both treatment need and survival. Furthermore, germline variability in pathways such as apoptosis, cell-cycle, pentose phosphate, GNα13 and Nitric oxide was associated with overall survival. Conclusion Our results support the existence of inherited conditionants of CLL evolution and points towards genes and pathways that may results useful as biomarkers of disease outcome. More research is needed to validate these findings.S

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools