Application Of Precision Medicine Research Articles

Current Context of Designing Phototheranostic Cyclometalated Iridium (III) Complexes to Open a New Avenue in Cancer Therapy.

Photo-induced chemotherapy offers the best option for the selective treatment of cancer among all the prevailing modalities. Iridium (III) complexes, flourished with excellent photophysical and photochemical properties, have been considered to be superior for undergoing photo-responsive cancer therapy. Large Stokes shift, long-lived triplet excited state, photostability, and tuneable emission have rendered its excellence as a phototheranostic agent. In particular, the cyclometalated Ir (III) complexes and their respective nanoparticles have made a strong niche in the arena of cancer therapy. In recent years, Ir (III) based complexes have shown promising utilities as both imaging and therapeutic agents as well. Therefore, this review summarises the recent advances in the strategic designing of cyclometalated Ir(III) complexes to augment their phototheranostic applications in precision medicine.

Contemporary and prospective use of azathioprine (AZA) in viral, rheumatic, and dermatological disorders: a review of pharmacogenomic and nanotechnology applications.

Azathioprine (AZA) has been extensively used for immunomodulatory effects in autoimmune disorders and transplantation. This article is proposed to review the contemporary and prospective use of AZA in viral, rheumatic, and dermatological disorders. The primary objective is to draw attention to possible developments in regards to AZA application in recent years, with an emphasis on the use of pharmacogenomics and nanotechnology to improve its efficacy in practice. This study reveals that AZA, having the active metabolites 6-mercaptopurine (6-MP) and 6-thioguanine (6-TG), may be useful in the treatment of systemic lupus erythematosus (SLE), pemphigus vulgaris, and psoriasis. Pharmacogenomic testing of thiopurine methyltransferase (TPMT) and Nudix hydrolase 15 (NUDT15) genotypes minimizes the occurrence of myelosuppression. Furthermore, new formulations of AZA using biocompatible polymers and nanoparticles for drug delivery were reported to improve its efficacy and lower systemic toxicity. This paper aims to establish the multifunctional nature of AZA in modern medicine, thus emphasizing its potential for other applications. Through the combination of pharmacogenomic analysis along with nanotechnology application, AZA makes the promise of enhancing patients' treatment efficacy and extending the stock of medical information available. These advancements offer new possibilities for application of precision medicine and improvements in the use of AZA therapy.

Exploring gender differences in pharmacokinetics of central nervous system related medicines based on a systematic review approach.

Due to the inevitable differences in physiological and/or genetic factors between genders, the possibility that differences in pharmacokinetics between genders may occur when exposed to the same dose of the same drug is subject to reasonable inference and suspicion. Nevertheless, a significant number of medicines still rely on empirical usage and uniform clinical application without consideration of inter-individual diversity factors. In particular, in the pharmacokinetic diversity of medicines related to central nervous system (CNS) activity, consideration of gender factors and access to comparative analysis are very limited. The purpose of this study was to conduct an integrated analysis and review of differences in pharmacokinetics between genders that have not been specifically reported to date for medicines related to CNS effects, which are a group of drugs with relatively significant concerns about systemic side effects. This study was accessible through extensive data collection and analyzes using a web-based scientific literature search engine of pharmacokinetic results of CNS-related drugs performed on humans, taking gender into account. As a result, significant differences in pharmacokinetics between genders were identified for many drugs related to CNS. And most of the pharmacokinetic differences between genders suggested a higher in vivo exposure in females. This study suggests that consideration of gender factors cannot be ignored and will be an important point of interest in the precision medicine application of CNS-related medicines.

Radiomics in precision medicine for colorectal cancer: a bibliometric analysis (2013–2023)

BackgroundThe incidence and mortality of colorectal cancer (CRC) have been rising steadily. Early diagnosis and precise treatment are essential for improving patient survival outcomes. Over the past decade, the integration of artificial intelligence (AI) and medical imaging technologies has positioned radiomics as a critical area of research in the diagnosis, treatment, and prognosis of CRC.MethodsWe conducted a comprehensive review of CRC-related radiomics literature published between 1 January 2013 and 31 December 2023 using the Web of Science Core Collection database. Bibliometric tools such as Bibliometrix, VOSviewer, and CiteSpace were employed to perform an in-depth bibliometric analysis.ResultsOur search yielded 1,226 publications, revealing a consistent annual growth in CRC radiomics research, with a significant rise after 2019. China led in publication volume (406 papers), followed by the United States (263 papers), whereas the United States dominated in citation numbers. Notable institutions included General Electric, Harvard University, University of London, Maastricht University, and the Chinese Academy of Sciences. Prominent researchers in this field are Tian J from the Chinese Academy of Sciences, with the highest publication count, and Ganeshan B from the University of London, with the most citations. Journals leading in publication and citation counts are Frontiers in Oncology and Radiology. Keyword and citation analysis identified deep learning, texture analysis, rectal cancer, image analysis, and management as prevailing research themes. Additionally, recent trends indicate the growing importance of AI and multi-omics integration, with a focus on improving precision medicine applications in CRC. Emerging keywords such as deep learning and AI have shown rapid growth in citation bursts over the past 3 years, reflecting a shift toward more advanced technological applications.ConclusionsRadiomics plays a crucial role in the clinical management of CRC, providing valuable insights for precision medicine. It significantly contributes to predicting molecular biomarkers, assessing tumor aggressiveness, and monitoring treatment efficacy. Future research should prioritize advancing AI algorithms, enhancing multi-omics data integration, and further expanding radiomics applications in CRC precision medicine.

Exploiting common patterns in diverse cancer types via multi-task learning

Cancer prognosis requires precision to identify high-risk patients and improve survival outcomes. Conventional methods struggle with the complexity of genetic biomarkers and diverse medical data. Our study uses deep learning to distil high-dimensional medical data into low-dimensional feature vectors exploring shared patterns across cancer types. We developed a multi-task bimodal neural network integrating RNA Sequencing and clinical data from three The Cancer Genome Atlas project datasets: Breast Invasive Carcinoma, Lung Adenocarcinoma, and Colon Adenocarcinoma. Our approach significantly improved prognosis prediction, especially for Colon Adenocarcinoma, with up to 26% increase in concordance index and 41% in the area under the precision-recall curve. External validation with Small Cell Lung Cancer achieved comparable metrics, indicating that supplementing small datasets with data from other cancers can improve performance. This work represents initial strides in using multi-task learning for prognosis prediction across cancer types, potentially revealing shared mechanisms among cancers and contributing to future applications in precision medicine.

Empirically establishing drug exposure records directly from untargeted metabolomics data.

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N -acyl lipid changes in a human immunodeficiency virus cohort. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

Should Routine Diagnostics Implement Gut Microbiota Analysis?

The gut microbiota plays a key role in human health. Dominated by the phyla Firmicutes and Bacteroidetes, its composition is highly individualized and influenced by diet, age, genetics, and the environment. The gut-liver axis highlights the bidirectional re-lationship between the gut and the liver, impacting metabolic homeostasis and immune reg-ulation. Gut dysbiosis, an imbalance in the gut microflora, contributes to liver diseases by disrupting gut barrier function and bile acid metabolism, leading to inflammation and fibro-genesis. Advancements in omics approaches, such as metagenomics, metatranscriptomics, metaproteomics, and metabolomics, have enhanced our understanding of the gut microbi-ota. These approaches offer insights into microbial composition and function, although they vary in cost, efficiency, and complexity. Metagenomics is widely used for its cost-effec-tiveness and rapid turnaround time despite limitations in taxonomic resolution, while meta-transcriptomics, metaproteomics, and metabolomics offer functional and metabolic insights but require sophisticated techniques and expertise. The Firmicutes/Bacteroidetes (F/B) ratio is a potential biomarker of gut dysbiosis linked to obesity, type 2 diabetes mellitus, and liver diseases. However, its diagnostic reliability is debated due to variations in individual factors and a lack of data on its associations with several diseases. Future research should focus on integrating multi-omics approaches so as to provide a holistic view of the gut microbiota and its role in health and disease, aiming for applications in precision medicine. While promising, the F/B ratio should be used cautiously alongside other diagnostic measures. In addition, renewed efforts are needed to develop cost-effective and rapid analysis methods for clinical use.

VarClaMM: A reference meta-model to understand DNA variant classification

Determining the significance of a DNA variant in patients’ health status – a complex process known as variant classification – is highly critical for precision medicine applications. However, there is still debate on how to combine and weigh diverse available evidence to achieve proper and consistent conclusions. Indeed, currently, there are more than 200 different variant classification guidelines available to the scientific community, aiming to establish a framework for standardizing the classification process. Yet, these guidelines are qualitative and vague by nature, hindering their practical application and potential automation. Consequently, more precise definitions are needed.In this work, we discuss our efforts to create VarClaMM, a UML meta-model that aims to provide a clear specification of the key concepts involved in variant classification, serving as a common framework for the process. Through this accurate characterization of the domain, we were able to find contradictions or inconsistencies that might have an effect on the classification results. VarClaMM’s conceptualization efforts will lay the ground for the operationalization of variant classification, enabling any potential automation to be based on precise definitions.

Relevance of Measured GFR in the Onconephrological Panorama: An Essential Tool to Apply Precision Medicine

Relevance of Measured GFR in the Onconephrological Panorama: An Essential Tool to Apply Precision Medicine

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

Abstract C085: Somatic mutational profiles of breast tumors from Hispanic/Latino women

Abstract Background: Breast cancer is the leading cause of cancer mortality among Hispanic/Latina (H/L) women. Precision medicine holds promise for more effective treatments by targeting therapies based on somatic mutations, copy number changes, mutational signatures, and/or germline risk variants. However, there is a paucity of genomics data for H/L women with breast cancer leading to a disparity in the application of precision medicine. We have previously characterized mutational profiles of 146 breast tumors from 140 H/L women. Here, we analyze data from whole exome tumor/normal sequencing and tumor RNA sequencing from 430 self-reported H/L breast cancer cases and compare their profiles to 385 tumors from self-reported White breast cancer cases. Methods: All participants were treated at City of Hope and all sequencing data were generated using Ashion GEM ExTra (Exact Sciences). Tumor/germline sequence data were aligned to the human genome (Build37) and variants were called using the FreeBayes workflow. To determine significantly mutated genes, we used MutSigCV and dNdS. We used Fisher’s exact test to determine if the frequency of somatic mutations was significantly different among H/L women and White women and corrected for multiple hypothesis testing using Benjamini-Hochberg false discovery rate (FDR). Results: Seventeen genes and 19 genes had q-values < 0.05 in H/L and White samples, respectively. Of those, AKT1, CBFB, FOXA1, MAP2K4, MAP3K1, MLL3, PIK3CA, PTEN, RB1, RUNX1, TBX3, TP53, and ZFPM1 were significantly mutated in both. CTCF and HS6ST1 were significantly mutated only in tumors from H/L. ARID1A, CDKN1B, NCOR1, and PRRT2 were significantly mutated only in tumors from White women. There was a significantly higher frequency of TP53 mutations (FDR q-value = 0.007) and CTCF mutations (FDR q-value = 0.057) in tumors from H/L compared to tumors from Whites. Conclusions: We found TP53 and CTCF to be more commonly mutated in tumors from H/L women. CTCF mutations are seen in other tumor types, but our study is the first to demonstrate that this gene is significantly mutated in breast cancer in any population. Since CTCF is a broadly active transcriptional regulator, tumors with these mutations may be associated with broad dysregulation of gene expression. Citation Format: Yuan Chun Ding, Shu Tao, Allen Mao, Elad Ziv, Susan L. Neuhausen. Somatic mutational profiles of breast tumors from Hispanic/Latino women [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C085.

Organoids: development and applications in disease models, drug discovery, precision medicine, and regenerative medicine.

Organoids are miniature, highly accurate representations of organs that capture the structure and unique functions of specific organs. Although the field of organoids has experienced exponential growth, driven by advances in artificial intelligence, gene editing, and bioinstrumentation, a comprehensive and accurate overview of organoid applications remains necessary. This review offers a detailed exploration of the historical origins and characteristics of various organoid types, their applications-including disease modeling, drug toxicity and efficacy assessments, precision medicine, and regenerative medicine-as well as the current challenges and future directions of organoid research. Organoids have proven instrumental in elucidating genetic cell fate in hereditary diseases, infectious diseases, metabolic disorders, and malignancies, as well as in the study of processes such as embryonic development, molecular mechanisms, and host-microbe interactions. Furthermore, the integration of organoid technology with artificial intelligence and microfluidics has significantly advanced large-scale, rapid, and cost-effective drug toxicity and efficacy assessments, thereby propelling progress in precision medicine. Finally, with the advent of high-performance materials, three-dimensional printing technology, and gene editing, organoids are also gaining prominence in the field of regenerative medicine. Our insights and predictions aim to provide valuable guidance to current researchers and to support the continued advancement of this rapidly developing field.

Revealing the mechanisms of RAC3 in tumor aggressiveness, the immunotherapy response, and drug resistance in bladder cancer.

Bladder cancer (BLCA) is a prevalent urinary tract malignancy with a high propensity for recurrence and chemoresistance. The molecular mechanisms underlying its progression and response to therapy have not been fully elucidated. We conducted a multifaceted analysis, integrating immunohistochemical (IHC) staining, bioinformatics evaluation using TCGA and CCLE databases, and in vitro assays using the BLCA cell lines 5637 and T24. RAC3 expression was assessed relative to clinical and pathological features. Functional enrichment analyses and gene set enrichment analysis (GSEA) were performed to identify associated biological processes and pathways. The impacts of RAC3 on cell proliferation, migration, invasion, and the immune microenvironment were evaluated using siRNA knockdown, CCK-8, Transwell, wound healing and colony formation assays. Elevated RAC3 expression was significantly correlated with an advanced tumor stage, lymph node metastasis, and poor prognosis for BLCA patients. The functional enrichment analysis implicated RAC3 in immune cell infiltration and immune checkpoint mechanisms. Notably, RAC3 knockdown significantly reduced the proliferative, migratory, and invasive capabilities of BLCA cells. These effects were reversed by the overexpression of RAC3. Additionally, RAC3 expression was linked to chemoresistance, with high RAC3 expression predicting resistance to certain therapeutic agents. The TIDE algorithm indicated that RAC3 expression could be a predictive biomarker for the immunotherapy response. RAC3 was identified as a potential therapeutic target and biomarker of BLCA, as its expression significantly influenced tumor progression, the immune response, and chemosensitivity. Targeting RAC3 may provide a novel strategy for the management of BLCA, particularly for patients resistant to conventional therapies. Further research is essential to elucidate the detailed mechanisms of RAC3 in BLCA and explore its clinical application in precision medicine.

Cancer Patient-Derived Cell-Based Models: Applications and Challenges in Functional Precision Medicine.

The field of oncology has witnessed remarkable progress in personalized cancer therapy. Functional precision medicine has emerged as a promising avenue for achieving superior treatment outcomes by integrating omics profiling and sensitivity testing of patient-derived cancer cells. This review paper provides an in-depth analysis of the evolution of cancer-directed drugs, resistance mechanisms, and the role of functional precision medicine platforms in revolutionizing individualized treatment strategies. Using two-dimensional (2D) and three-dimensional (3D) cell cultures, patient-derived xenograft (PDX) models, and advanced functional assays has significantly improved our understanding of tumor behavior and drug response. This progress will lead to identifying more effective treatments for more patients. Considering the limited eligibility of patients based on a genome-targeted approach for receiving targeted therapy, functional precision medicine provides unprecedented opportunities for customizing medical interventions according to individual patient traits and individual drug responses. This review delineates the current landscape, explores limitations, and presents future perspectives to inspire ongoing advancements in functional precision medicine for personalized cancer therapy.

Timing of Continuous Renal Replacement Therapy Initiation in Sepsis-Associated Acute Kidney Injury: A Comprehensive Review and Future Directions

This review examines the application of continuous renal replacement therapy (CRRT) in patients with sepsis-associated acute kidney injury (S-AKI), with a particular focus on the timing of CRRT initiation. This review addresses the controversy surrounding initiation timing and proposes future research directions. Through a systematic review of recent literature on CRRT for S-AKI, working principles, therapeutic mechanisms, initiation timing of CRRT, and related meta-analyses were summarized. Current studies indicate that the optimal timing for CRRT initiation in S-AKI patients remains inconclusive, with ongoing debate regarding whether early initiation significantly improves patient survival and renal function. This lack of consensus reflects the heterogeneity of the S-AKI patient population and the limitations of existing research methodologies. Future studies should focus on advancing the application of precision medicine in S-AKI and developing individualized treatment strategies by integrating multidimensional information to optimize CRRT utilization and improve patient outcomes.

Extracting regulatory active chromatin footprint from cell-free DNA

Cell-free DNA (cfDNA) has emerged as a pivotal player in precision medicine, revolutionizing the diagnostic and therapeutic landscape. While its clinical applications have significantly increased in recent years, current cfDNA assays have limited ability to identify the active transcriptional programs that govern complex disease phenotypes and capture the heterogeneity of the disease. To address these limitations, we have developed a non-invasive platform to enrich and examine the active chromatin fragments (cfDNAac) in peripheral blood. The deconvolution of the cfDNAac signal from traditional nucleosomal chromatin fragments (cfDNAnuc) yields a catalog of features linking these circulating chromatin signals in blood to specific regulatory elements across the genome, including enhancers, promoters, and highly transcribed genes, mirroring the epigenetic data from the ENCODE project. Notably, these cfDNAac counts correlate strongly with RNA polymerase II activity and exhibit distinct expression patterns for known circadian genes. Additionally, cfDNAac signals across gene bodies and promoters show strong correlations with whole blood gene expression levels defined by GTEx. This study illustrates the utility of cfDNAac analysis for investigating epigenomics and gene expression, underscoring its potential for a wide range of clinical applications in precision medicine.

A framework for sharing of clinical and genetic data for precision medicine applications.

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients' biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype-phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype-phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

The Role of Artificial Intelligence and Machine Learning in Cardiovascular Imaging and Diagnosis.

Cardiovascular diseases remain the leading cause of global mortality, underscoring the critical need for accurate and timely diagnosis. This narrative review examines the current applications and future potential of artificial intelligence (AI) and machine learning (ML) in cardiovascular imaging. We discuss the integration of these technologies across various imaging modalities, including echocardiography, computed tomography, magnetic resonance imaging, and nuclear imaging techniques. The review explores AI-assisted diagnosis in key areas such as coronary artery disease detection, valve disorders assessment, cardiomyopathy classification, arrhythmia detection, and prediction of cardiovascular events. AI demonstrates promise in improving diagnostic accuracy, efficiency, and personalized care. However, significant challenges persist, including data quality standardization, model interpretability, regulatory considerations, and clinical workflow integration. We also address the limitations of current AI applications and the ethical implications of their implementation in clinical practice. Future directions point towards advanced AI architectures, multimodal imaging integration, and applications in precision medicine and population health management. The review emphasizes the need for ongoing collaboration between clinicians, data scientists, and policymakers to realize the full potential of AI in cardiovascular imaging while ensuring ethical and equitable implementation. As the field continues to evolve, addressing these challenges will be crucial for the successful integration of AI technologies into cardiovascular care, potentially revolutionizing diagnostic capabilities and improving patient outcomes.

Large-Scale Genomics Applications in Public Health and Precision Medicine Using Big Data and Predictive Analytics

After gaining experience from large population sequencing projects, there is a significant gap between the promise of genomics in Precision Medicine (PM) and the existing state of affairs. To fully exploit this potential, Public Healthcare (PH) must adopt procedures, regulations, and technology that are unfamiliar to them. The development needs to be improved by several historical procedures and regulatory hurdles. In the future, PM will include making genetic big data easily accessible at the point of treatment and implementing tools to govern its use effectively. Significant modifications need to be made in billing, payment, and monitoring to accomplish this goal. New systemic and technological structures must also be developed inside the healthcare sector. The responsibilities of clinical geneticists will expand to include overseeing PM systems. Genetic counselors will guide and assist PM by establishing and upholding PM structures. The trajectory of the research is now hindered by several impediments, resulting in avoidable fatalities. Revamping medical facilities to accommodate genomics has the potential to influence the treatment of patients significantly and enable the achievement of long-awaited advancements in PM.

CRISPR/Gal4BD-Cas donor adapting systems based on miniaturized Cas proteins for improved gene editing.

Targeted precise point editing and knock-in can be achieved by homology-directed repair(HDR) based gene editing strategies in mammalian cells. However, the inefficiency of HDR strategies seriously restricts their application in precision medicine and molecular design breeding. In view of the problem that exogenous donor DNA cannot be efficiently recruited autonomously at double-stranded breaks(DSBs) when using HDR strategies for gene editing, the concept of donor adapting system(DAS) was proposed and the CRISPR/Cas9-Gal4BD DAS was developed previously. Due to the large size of SpCas9 protein, its fusion with the Gal4BD adaptor is inconvenient for protein expression, virus vector packaging and in vivo delivery. In this study, two novel CRISPR/Gal4BD-SlugCas9 and CRISPR/Gal4BD-AsCas12a DASs were further developed, using two miniaturized Cas proteins, namely SlugCas9-HF derived from Staphylococcus lugdunensis and AsCas12a derived from Acidaminococcus sp. Firstly, the SSA reporter assay was used to assess the targeting activity of different Cas-Gal4BD fusions, and the results showed that the fusion of Gal4BD with SlugCas9 and AsCas12a N-terminals had minimal distraction on their activities. Secondly, the HDR efficiency reporter assay was conducted for the functional verification of the two DASs and the corresponding donor patterns were optimized simultaneously. The results demonstrated that the fusion of the Gal4BD adaptor binding sequence at the 5'-end of intent dsDNA template (BS-dsDNA) was better for the CRISPR/Gal4BD-AsCas12a DAS, while for the CRISPR/Gal4BD-SlugCas9 DAS, the dsDNA-BS donor pattern was recommended. Finally, CRISPR/Gal4BD-SlugCas9 DAS was used to achieve gene editing efficiency of 24%, 37% and 31% respectively for EMX1, NUDT5 and AAVS1 gene loci in HEK293T cells, which was significantly increased compared with the controls. In conclusion, this study provides a reference for the subsequent optimization of the donor adapting systems, and expands the gene editing technical toolbox for the researches on animal molecular design breeding.