Application of machine learning in combination with mechanistic modeling to predict plasma exposure of small molecules

Prediction of a new molecule’s exposure in plasma is a critical first step toward understanding its efficacy/toxicity profile and concluding whether it is a possible first-in-class, best-in-class candidate. For this prediction, traditional pharmacometrics use a variety of scaling methods that are heavily based on pre-clinical pharmacokinetic (PK) data. We here propose a novel framework based on which preclinical exposure prediction is performed by applying machine learning (ML) in tandem with mechanism-based modeling. In our proposed method, a relationship is initially established between molecular structure and physicochemical (PC)/PK properties using ML, and then the ML-driven PC/PK parameters are used as input to mechanistic models that ultimately predict the plasma exposure of new candidates. To understand the feasibility of our proposed framework, we evaluated a number of mechanistic models (1-compartment, physiologically based pharmacokinetic (PBPK)), PBPK distribution models (Berezhkovskiy, PK-Sim standard, Poulin and Theil, Rodgers and Rowland, and Schmidt), and PBPK parameterizations (using in vivo, or in vitro clearance). For most of the scenarios tested, our results demonstrate that PK profiles can be adequately predicted based on the proposed framework. Our analysis further indicates some limitations when liver microsomal intrinsic clearance (CLint) is used as the only clearance pathway and underscores the necessity of investigating the variability emanating from the different distribution models when providing PK predictions. The suggested approach aims at earlier exposure prediction in the drug development process so that critical decisions on molecule screening, chemistry design, or dose selection can be made as early as possible

DENDRITIC AND SPINAL PATHOLOGY OF THE PURKINJE CELLS FROM THE HUMAN CEREBELLAR VERMIS IN ALZHEIMER’S DISEASE

Background: Alzheimer’s disease constitutes one of the main causes of dementia. It is clinically characterized by memory impairment, deterioration of intellectual faculties and loss of professional skills. Furthermore changes in equilibrium and limb coordination are clinically demonstrable in persons with Alzheimer’s disease. In the present study we tried to figure out possible changes of the Purkinje cells in Alzheimer’s disease brains. Subjects and methods: We studied the Purkinje cells from the vermis of the cerebellum in 5 Alzheimer’ disease brains Golgi technique. Results: In the Purkinje cells from the inferior surface of the cerebellar hemispheres severe dendritic and spinal pathology consisting of loss of distal dendritic segments and alterations of dendritic spine morphology can be noticed in Alzheimer’s disease brains. Conclusions: The morphological and morphometric estimation of the dendrites and the dendritic spines of the Purkinje cells from the inferior surface of the cerebellar hemispheres in Alzheimer’s disease brains revealed substantial alterations of the dendritic arborization and marked loss of the dendritic spines, which may be related to cognitive impairment and motor deficits in Alheimer’s disease

The Impact of Stochasticity and Its Control on a Model of the Inflammatory Response

The dysregulation of inflammation, normally a self-limited response that initiates healing, is a critical component of many diseases. Treatment of inflammatory disease is hampered by an incomplete understanding of the complexities underlying the inflammatory response, motivating the application of systems and computational biology techniques in an effort to decipher this complexity and ultimately improve therapy. Many mathematical models of inflammation are based on systems of deterministic equations that do not account for the biological noise inherent at multiple scales, and consequently the effect of such noise in regulating inflammatory responses has not been studied widely. In this work, noise was added to a deterministic system of the inflammatory response in order to account for biological stochasticity. Our results demonstrate that the inflammatory response is highly dependent on the balance between the concentration of the pathogen and the level of biological noise introduced to the inflammatory network. In cases where the pro- and anti-inflammatory arms of the response do not mount the appropriate defense to the inflammatory stimulus, inflammation transitions to a different state compared to cases in which pro- and anti-inflammatory agents are elaborated adequately and in a timely manner. In this regard, our results show that noise can be both beneficial and detrimental for the inflammatory endpoint. By evaluating the parametric sensitivity of noise characteristics, we suggest that efficiency of inflammatory responses can be controlled. Interestingly, the time period on which parametric intervention can be introduced efficiently in the inflammatory system can be also adjusted by controlling noise. These findings represent a novel understanding of inflammatory systems dynamics and the potential role of stochasticity thereon

Salvage chemotherapy with high-dose leucovorin (LV) and 48-hour continuous infusion (CI) of 5-fluorouracil (5-FU) in combination with conventional doses of cyclophosphamide (CPM) in patients with metastatic breast cancer (MBC) pretreated with anthracycline and taxanes

The purpose of this study was to evaluate the activity and tolerance of high-dose leucovorin (LV) and infusional 5-fluorouracil (5-FU) in combination with conventional doses of cyclophosphamide (CPM) as salvage chemotherapy in patients with metastatic breast cancer (MBC) pretreated with anthracyclines and taxanes. 41 patients (median age 59 years) with MBC refractory or resistant to anthracyclines and taxanes were enrolled. The patients' performance status (WHO) was 0 in 10 patients (24%), 1 in 22 (54%), and 2 in 9 (22%). 30 (73%) patients had received 2 or more prior chemotherapy regimens. Cyclophosphamide (600 mg m−2) was given i.v. bolus on day 1 and LV (500 mg m−2 d−1) as a 2-h infusion followed by 5-FU (1.5 g m−2 d−1) over a 22 h c.i. for 2 consecutive days. Cyclophosphamide was administered every 28 days while 5-FU/LV every 14 days. In an intention-to-treat analysis, complete response (CR) was achieved in 2 (4.9%) patients and partial response (PR) in 9 (22%) (overall response rate 26.9%; 95% CI: 13.27–40.39%). Stable disease (SD) and progressive disease (PD) were observed in 9 (22%) and 21 (51%) patients, respectively. The overall response rate was 6% and 40% in patients with primary and secondary resistance to anthracyclines/taxanes, respectively (P = 0.047). The median duration of response and the median time to disease progression was 8 and 9.5 months, respectively. The median overall survival was 13 months and the probability for 1-year survival 51%. Grade 3/4 neutropenia occurred in 9 (22%) patients and 4 (9%) patients developed grade 3/4 thrombocytopenia. Non-haematological toxicity was mild. There were no cases of febrile neutropenia, toxic deaths or treatment-related hospital admissions due to toxicity. The combination of high-dose 5-FU/LV with conventional doses of cyclophosphamide is a well tolerated and effective salvage regimen in patients with MBC heavily pretreated with both anthracyclines and taxanes. © 2001 Cancer Research Campaignhttp://www.bjcancer.comhttp://www.bjcancer.co

Generation of Functional CLL-Specific Cord Blood CTL Using CD40-Ligated CLL APC

PMCID: PMC3526610This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

A dose escalation and pharmacokinetic study of biweekly pegylated liposomal doxorubicin, paclitaxel and gemcitabine in patients with advanced solid tumours

To determine the maximum tolerated doses (MTDs) and dose-limiting toxicities (DLTs) of pegylated liposomal doxorubicin (PLD), paclitaxel (PCX) and gemcitabine (GEM) combination administered biweekly in patients with advanced solid tumours. Twenty-two patients with advanced-stage solid tumours were treated with escalated doses of PLD on day 1 and PCX plus GEM on day 2 (starting doses: 10, 100 and 800 mg m−2, respectively) every 2 weeks. DLTs and pharmacokinetic (PK) parameters of all drugs were determined during the first cycle of treatment. All but six (73%) patients had previously received at least one chemotherapy regimen. The DLT dose level was reached at PLD 12 mg m−2, PCX 110 mg m−2 and GEM 1000 mg m−2 with neutropaenia being the dose-limiting event. Of the 86 chemotherapy cycles delivered, grade 3 and 4 neutropaenia occurred in 20% with no cases of febrile neutropaenia. Non-haematological toxicities were mild. The recommended MTDs are PLD 12 mg m−2, PCX 100 mg m−2 and GEM 1000 mg m−2 administered every 2 weeks. The PK data revealed no obvious drug interactions. Biweekly administration of PLD, PCX and GEM is a well-tolerated chemotherapy regimen, which merits further evaluation in various types of solid tumours

Microparticles in acute coronary syndrome

BACKGROUND: Emerging evidence supports the role of cell-derived microparticles (MPs) in the pathophysiology of acute coronary syndrome (ACS). OBJECTIVES: To explore the relationship between coronary and systemic MP levels, investigate the correlation between MPs, inflammatory markers and Troponin T in patients with ACS. METHODS: Thirty seven patients with ACS scheduled for percutaneous coronary interventions (PCI) were studied. Eleven patients with stable angina (SA) were included as a control group. AnnexinV+MPs (AnV+MPs) and activated platelet-monocyte aggregates (PMA) from right atrium (RA) and culprit coronary artery (CO) distal to culprit lesion were measured using flow cytometry. High sensitivity C-reactive protein (CRP), Interleukin - 6 (IL-6), tumour necrosis factor - α (TNF-α), serum amyloid A (SAA) and Troponin T were assayed. RESULTS: Total and cell specific AnV+MP expression were higher in the ACS group in both the CO and RA, with greater levels detected in the CO. Platelet activation showed positive correlation with Troponin-T and platelet MP in both CO and RA of the ACS group (r=0.4 for both; p=0.04 & p=0.03 respectively). Inflammatory markers levels did not differ between the ACS and SA patients. CONCLUSIONS: Elevated coronary and systemic MP levels and positive correlation of platelet activation with Troponin-T and platelet MPs suggest a pathogenic role for MPs in ACS

Serial analysis of circulating tumor cells in metastatic breast cancer receiving first-line chemotherapy

Background: We examined the prognostic significance of circulating tumor cell (CTC) dynamics during treatment in metastatic breast cancer (MBC) patients receiving first-line chemotherapy. Methods: Serial CTC data from 469 patients (2,202 samples) were used to build a novel latent mixture model to identify groups with similar CTC trajectory (tCTC) patterns during the course of treatment. Cox regression was used to estimate hazard ratios for progression-free survival (PFS) and overall survival (OS) in groups based on baseline CTCs (bCTC), combined CTC status at baseline to the end of cycle 1 (cCTC), and tCTC. Akaike Information Criterion (AIC) was used to select the model that best predicted PFS and OS. Results: Latent mixture modeling revealed 4 distinct tCTC patterns: undetectable CTCs (tCTCneg, 56.9% ), low (tCTClo, 23.7%), intermediate (tCTCmid, 14.5%), or high (tCTChi, 4.9%). Patients with tCTClo, tCTCmid and tCTChi patterns had statistically significant inferior PFS and OS compared to those with tCTCneg (P<.001). AIC indicated that the tCTC model best predicted PFS and OS when compared to bCTC and cCTC models. Validation studies in an independent cohort of 1,856 MBC patients confirmed these findings. Further validation using only a single pretreatment CTC measurement confirmed prognostic performance of the tCTC model. Conclusions: We identified four novel prognostic groups in MBC based on similarities in CTC trajectory patterns during chemotherapy. Prognostic groups included patients with very poor outcome (tCTCmid+tCTChi, 19.4%) who could benefit from more effective treatment. Our novel prognostic classification approach may be utilized for fine-tuning of CTC-based risk-stratification strategies to guide future prospective clinical trials in MBC