Plasma and brain pharmacokinetics of letrozole and drug interaction studies with temozolomide in NOD-scid gamma mice and sprague dawley rats

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

Brain-Derived Estrogen Regulates Neurogenesis, Learning and Memory with Aging in Female Rats

Although 17β-estradiol (E2) can be locally synthesized in the brain, whether and how brain-derived E2 (BDE2) impacts neurogenesis with aging is largely unclear. In this study, we examined the hippocampal neural stem cells, neurogenesis, and gliogenesis of 1, 3, 6, 14, and 18-month (Mon) female rats. Female forebrain neuronal aromatase knockout (FBN-ARO-KO) rats and letrozole-treated rats were also employed. We demonstraed that (1) the number of neural stem cells declined over 14-Mon age, and the differentiation of astrocytes and microglia markedly elevated and exhibited excessive activation. KO rats showed declines in astrocyte A2 subtype and elevation in A1 subtype at 18 Mon; (2) neurogenesis sharply dropped from 1-Mon age; (3) KO suppressed dentate gyrus (DG) neurogenesis at 1, 6 and 18 Mon. Additionally, KO and letrozole treatment led to declined neurogenesis at 1-Mon age, compared to age-matched WT controls; (4) FBN-ARO-KO inhibited CREB-BDNF activation, and decreased protein levels of neurofilament, spinophilin and PSD95. Notably, hippocampal-dependent spatial learning and memory was impaired in juvenile (1 Mon) and adulthood (6 Mon) KO rats. Taken together, we demonstrated that BDE2 plays a pivotal role for hippocampal neurogenesis, as well as learning and memory during female aging, especially in juvenile and middle age.

Potentiation of temozolomide activity against glioblastoma cells by aromatase inhibitor letrozole

PURPOSE

The DNA alkylating agent temozolomide (TMZ), is the first-line therapeutic for the treatment of glioblastoma (GBM). However, its use is confounded by the occurrence of drug resistance and debilitating adverse effects. Previously, we observed that letrozole (LTZ), an aromatase inhibitor, has potent activity against GBM in pre-clinical models. Here, we evaluated the effect of LTZ on TMZ activity against patient-derived GBM cells.

METHODS

Employing patient-derived G76 (TMZ-sensitive), BT142 (TMZ-intermediately sensitive) and G43 and G75 (TMZ-resistant) GBM lines we assessed the influence of LTZ and TMZ on cell viability and neurosphere growth. Combination Index (CI) analysis was performed to gain quantitative insights of this interaction. We then assessed DNA damaging effects by conducting flow-cytometric analysis of ˠH2A.X formation and induction of apoptotic signaling pathways (caspase3/7 activity). The effects of adding estradiol on LTZ-induced cytotoxicity and DNA damage were also evaluated.

RESULTS

Co-treatment with LTZ at a non-cytotoxic concentration (40 nM) reduced TMZ IC50 by 8, 37, 240 and 640 folds in G76, BT-142, G43 and G75 cells, respectively. The interaction was deemed to be synergistic based on CI analysis. LTZ co-treatment also significantly increased DNA damaging effects of TMZ. Addition of estradiol abrogated these LTZ effects.

CONCLUSIONS

LTZ increases DNA damage and synergistically enhances TMZ activity in TMZ sensitive and TMZ-resistant GBM lines. These effects are abrogated by the addition of exogenous estradiol underscoring that the observed effects of LTZ may be mediated by estrogen deprivation. Our study provides a strong rationale for investigating the clinical potential of combining LTZ and TMZ for GBM therapy.

The Extension of the LeiCNS-PK3.0 Model in Combination with the "Handshake" Approach to Understand Brain Tumor Pathophysiology

Micrometastatic brain tumor cells, which cause recurrence of malignant brain tumors, are often protected by the intact blood–brain barrier (BBB). Therefore, it is essential to deliver effective drugs across not only the disrupted blood-tumor barrier (BTB) but also the intact BBB to effectively treat malignant brain tumors. Our aim is to predict pharmacokinetic (PK) profiles in brain tumor regions with the disrupted BTB and the intact BBB to support the successful drug development for malignant brain tumors. LeiCNS-PK3.0, a comprehensive central nervous system (CNS) physiologically based pharmacokinetic (PBPK) model, was extended to incorporate brain tumor compartments. Most pathophysiological parameters of brain tumors were obtained from literature and two missing parameters of the BTB, paracellular pore size and expression level of active transporters, were estimated by fitting existing data, like a “handshake”. Simultaneous predictions were made for PK profiles in extracellular fluids (ECF) of brain tumors and normal-appearing brain and validated on existing data for six small molecule anticancer drugs. The LeiCNS-tumor model predicted ECF PK profiles in brain tumor as well as normal-appearing brain in rat brain tumor models and high-grade glioma patients within twofold error for most data points, in combination with estimated paracellular pore size of the BTB and active efflux clearance at the BTB. Our model demonstrated a potential to predict PK profiles of small molecule drugs in brain tumors, for which quantitative information on pathophysiological alterations is available, and contribute to the efficient and successful drug development for malignant brain tumors.

High-throughput glycolytic inhibitor discovery targeting glioblastoma by graphite dots-assisted LDI mass spectrometry

Malignant tumors will become vulnerable if their uncontrolled biosynthesis and energy consumption engaged in metabolic reprogramming can be cut off. Here, we report finding a glycolytic inhibitor targeting glioblastoma with graphite dots-assisted laser desorption/ionization mass spectrometry as an integrated drug screening and pharmacokinetic platform (GLMSD). We have performed high-throughput virtual screening to narrow an initial library of 240,000 compounds down to the docking of 40 compounds and identified five previously unknown chemical scaffolds as promising hexokinase-2 inhibitors. The best inhibitor (Compd 27) can regulate the reprogrammed metabolic pathway in U87 glioma cells (median inhibitory concentration ~ 11.3 μM) for tumor suppression. Highly effective therapy against glioblastoma has been demonstrated in both subcutaneous and orthotopic brain tumors by synergizing Compd 27 and temozolomide. Our glycolytic inhibitor discovery can inspire personalized medicine targeting reprogrammed metabolisms of malignant tumors. GLMSD enables large, high-quality data for next-generation artificial intelligence-aided drug development.

Gender-based differences in brain and plasma pharmacokinetics of letrozole in sprague-dawley rats: Application of physiologically-based pharmacokinetic modeling to gain quantitative insights

Based on the discovery that the estrogen synthase aromatase (CYP19A1) is abundantly expressed in high- grade gliomas, the aromatase inhibitor, letrozole is being investigated in pre-clinical models as a novel agent against this malignancy. Here, we investigated the systemic and brain pharmacokinetics of letrozole following single and steady state dosing in both male and female Sprague-Dawley rats. Furthermore, we employed physiologically-based pharmacokinetic (PBPK) modeling to gain quantitative insights into the blood-brain barrier penetration of this drug. Letrozole (4 mg/kg) was administered intraperitoneally daily for 5 days (for males) and 11 days (for females) and intracerebral microdialysis was performed for brain extracellular fluid (ECF) collection simultaneously with venous blood sampling. Drug levels were measured using HPLC and non-compartmental analysis was conducted employing WinNonlin®. Simcyp animal simulator was used for conducting bottom-up PBPK approach incorporating the specified multi-compartment brain model. Overall, marked gender-specific differences in the systemic and brain pharmacokinetics of letrozole were observed. Letrozole clearance was much slower in female rats resulting in markedly higher plasma and brain drug concentrations. At steady state, the plasma AUC 0-24 was 103.0 and 24.8 μg*h/ml and brain ECF AUC 0-12 was 24.0 and 4.8 μg*h/ml in female and male rats, respectively. The PBPK model simulated brain concentration profiles were in close agreement with the observed profiles. While gender-specific differences in letrozole PK are not observed in the clinical setting, these findings will guide the dose optimization during pre-clinical investigations of this compound. The PBPK model will serve as an important clinical translational tool.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Cerebral Microdialysis as a Tool for Assessing the Delivery of Chemotherapy in Brain Tumor Patients

The development of curative treatment for glioblastoma has been extremely challenging. Chemotherapeutic agents that have seemed promising have failed in clinical trials. Drugs that can successfully target cancer cells within the brain must first traverse the brain interstitial fluid. Cerebral microdialysis (CMD) is an invasive technique in which interstitial fluid can be directly sampled. CMD has primarily been used clinically in the setting of head trauma and subarachnoid hemorrhage. Our goal was to review the techniques, principles, and new data pertaining to CMD to highlight its use in neuro-oncology. We conducted a literature search using the PubMed database and selected studies in which the investigators had used CMD in either animal brain tumor models or clinical trials. The references were reviewed for additional information. Studies of CMD have shown its importance as a neurosurgical technique. CMD allows for the collection of pharmacokinetic data on drug penetrance across the blood-brain barrier and metabolic data to characterize the response to chemotherapy. Although no complications have been reported, the current CMD technique (as with any procedure) has risks and limitations, which we have described in the present report. Animal CMD experiments have been used to exclude central nervous system drug candidates from progressing to clinical trials. At present, patients undergoing CMD have been monitored in the intensive care unit, owing to the requisite tethering to the apparatus. This can be expected to change soon because of advances in microminiaturization. CMD is an extremely valuable, yet underused, technique. Future CMD applications will have central importance in assessing drug delivery to tumor cells in vivo, allowing a pathway to successful therapy for malignant brain tumors.

Considering the Experimental use of Temozolomide in Glioblastoma Research

Temozolomide (TMZ) currently remains the only chemotherapeutic component in the approved treatment scheme for Glioblastoma (GB), the most common primary brain tumour with a dismal patient's survival prognosis of only ~15 months. While frequently described as an alkylating agent that causes DNA damage and thus-ultimately-cell death, a recent debate has been initiated to re-evaluate the therapeutic role of TMZ in GB. Here, we discuss the experimental use of TMZ and highlight how it differs from its clinical role. Four areas could be identified in which the experimental data is particularly limited in its translational potential: 1. transferring clinical dosing and scheduling to an experimental system and vice versa; 2. the different use of (non-inert) solvent in clinic and laboratory; 3. the limitations of established GB cell lines which only poorly mimic GB tumours; and 4. the limitations of animal models lacking an immune response. Discussing these limitations in a broader biomedical context, we offer suggestions as to how to improve transferability of data. Finally, we highlight an underexplored function of TMZ in modulating the immune system, as an example of where the aforementioned limitations impede the progression of our knowledge.