Toxicity, Drug

PK properties such as absorption, distribution, metabolism, excretion and toxicity (ADMET) profiling of compounds were determined using the pkCSM ADMET descriptors algorithm protocol¹ and the Discover Studio 4.0 (DS4.0) software package (Accelrys Software, Inc., San Diego, CA, United States). Two important chemical descriptors correlate well with PK properties, the2D polar surface area (PSA_2D, a primary determinant of fractional absorption) and the lipophilicity levels in the form of atom-based LogP (AlogP98). The absorption of drugs depends on factors including membrane permeability [indicated by colon cancer cell line (Caco-2)], intestinal absorption, skin permeability levels, P-glycoprotein substrate or inhibitor. The distribution of drugs depends on factors that include the blood–brain barrier (logBB), CNS permeability, and the volume of distribution (VDss). Metabolism is predicted based on the CYP models for substrate or inhibition (CYP2D6, CYP3A4, CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4). Excretion is predicted based on the total clearance model and renal OCT2 substrate. The toxicity of drugs is predicted based on AMES toxicity, hERG inhibition, hepatotoxicity, and skin sensitization. These parameters were calculated and checked for compliance with their standard ranges.
The prediction of genotoxicity used the OECD QSAR toolbox 4.1 software package (Organization for Economic Co-operation and Development, Paris, France) and Toxtree, Version 2.6.13 (Ideaconsult, Ltd., Sofia, Bulgaria). Both software are open source freely available in silico programs that identify the chemical structural alerts (SA).

Informative censoring occurs when individuals are lost to follow-up for reasons that may relate to their (unknown) outcome. For example, in a randomised trial in which the main outcome is time to cancer recurrence, a patient who is lost to follow-up may be more likely to have experienced drug toxicity or ill health and thus may also be more susceptible to (earlier) relapse. Informative censoring introduces bias into the standard methods discussed previously. Unfortunately, it is difficult both to identify informative censoring and to assess its impact. It is helpful though to know what proportion of censored individuals were lost to follow-up before the end of the study (Clark et al, 2002 (link)).
A simple, ad hoc approach to the problem is to perform sensitivity analyses, to assess the impact of assigning different survival times to those patients whose observed (censored) survival times may have been affected in this manner. For example, if a patient suspected to be in ill health exits the study at 4 weeks, a first analysis may be performed with this patient censored at 4 weeks and a second where the patient is assumed to have relapsed at 4 weeks (i.e. a ‘best case – worst case’ scenario). This approach works best when there are few such patients, but in that situation, the possible bias will be very small. Another possibility is to decide a priori that all such patients will be treated in a particular way. The issue has been of particular concern in randomised trials of nicotine replacement therapy, in which losses to follow-up are considerable. In a systematic review of randomised trials, patients who were lost to follow-up were regarded as being continuing smokers (Silagy et al, 2002 ).
More formal approaches have been proposed (e.g. Robins, 1995a (link), 1995b (link); Scharfstein et al, 2001 (link)). In general, they assume that a relationship exists (and can be modelled) between censoring times and baseline covariates and perhaps also post-treatment patient data. It is difficult to evaluate the assumptions of these complex methods, and implementation in statistical software is limited.
If follow-up stops because the patient has experienced a different defined event, the problem may be viewed as a competing risk scenario (see below), or handled via a mixture model (or ‘cure’ model), where the differing event types are explicitly modelled. The latter method makes particular sense if the two events are quite dissimilar, such as patient recovery and patient death.
In practice, if there is little informative censoring, the bias introduced to standard methods is minimal, and in general using these along with simply reporting loss to follow-up (perhaps with a basic sensitivity analysis) will suffice. Good patient follow-up and avoidance of unnecessary drop-out is by far the best solution, and when and why drop-out occurs should always be reported (Moher et al, 2001 (link)).

For K8484 and PANC-1, drug cytotoxicity in vitro was assessed by the means of Sulforhodamine B colorimetric (SRB) assay. Cells were plated with a range of concentrations. After 72 h of incubation at 37 °C, they were fixed (3% trichloroacetic acid in water (w/v), 90 minutes, 4 °C), washed in water and stained with a 0.057% SRB (Sigma) solution in acetic acid (w/v) for 30 minutes. The plates were washed (1% acetic acid (v/v), 4 times), and the protein-bound dye was dissolved in a 10 mM Tris base solution (pH 10.5). Fluorescence was measured using Tecan Infinite M200 plate-reader (excitation 488 nm, emission 585 nm). Percentage inhibition compared to solvent control-treated cells was calculated for each drug concentration.
For SKOV3, drug toxicity was assessed by cell titer glo. Approximately 500 cells per well in 5 uL was dispensed using a Multidrop Combi dispenser (Thermo Fisher Scientific) into 1,536 solid-bottom white Greiner Bio-one tissue culture-treated plates (catalog #789173-F). 23 nL of ). PF477736 was transferred to the assay plate using a Kalypsis pintool. Plates were covered with stainless steel cell culture lids and incubated at standard conditions for 48 hours. To assess viability, 3 uL of CellTiter Glo luminescent cell viability assay reagent (Promega) was added using a Bioraptor Flying Reagent Dispenser (Aurora Discovery-BD). The plates were incubated for 15 minutes at room temperature and measured using a 10-s exposure on a ViewLux (Perkin-Elmer).

The study population comprised three subpopulations; (1) Patients diagnosed with schizophrenia less than 2 years before inclusion in the study (patients diagnosed with SCZ < 2), (2) Psychiatric healthy controls (PHC) with no history of mental illness matched to patients diagnosed with SCZ < 2 on age, sex, and smoking status (smoker/non-smoker) at the time of inclusion, and (3) Patients diagnosed with schizophrenia 10 or more years before inclusion (patients diagnosed with SCZ ≥ 10). Patients with schizophrenia were recruited from the North Denmark Region. Only patients 18 years or older at inclusion diagnosed with schizophrenia or schizo-affective disorder (ICD-10 F20 or F25) and being able to give written informed consent, were included in the study. In Denmark, schizophrenia is defined according to ICD-10, which lists first-rank symptoms, delusions of bizarre characteristics or a combination of at least two of the following symptoms: hallucinations on a daily basis combined with delusions, catatonia, negative symptoms or disorganized thinking.
First-rank symptoms include auditory hallucinations commenting or conversing, somatic hallucinations, thought withdrawal or thought broadcasting. The presence of a brain disorder, drug intoxication or withdrawal hinder the diagnosis of schizophrenia.Patients were excluded if pregnant, breastfeeding or if they were unable to participate in the planned program for the primary cohort study [13 (link)].
Using a random recruitment approach, psychiatrically healthy controls were matched on age, sex, and smoking status to patients diagnosed with SCZ < 2. They were invited to participate in this comprehensive cardiac screening programme.
The clinical prospective cohort study has been approved by The North Denmark Region Committee on Health Research Ethics (N-20140047) and is consistent with the ethical standards of the Declaration of Helsinki 2013. The personal data categories collected by the project has been registered in the processing activities of research in the North Denmark Region in compliance with the European Union’s GDPR article 30.

After the patients enrolled themselves, their guardians signed the informed consent form. Before the initiation of treatment, the liver and kidney functions of the patients were examined to avoid contraindication of therapy. Bone marrow was collected under an aseptic condition, placed in an EDTA anticoagulant tube, refrigerated at 2 °C – 8 °C, and transported to PreceDo Inc. (Hefei, China). The primary tumor cells isolated and purified according to the standard operating procedure were expanded in vitro by an improved cell reprogramming technique. The cultured primary cells were tested for high-throughput drugs in vitro according to the clinical first-line and second-line treatment schemes of the corresponding cancer types and FDA drug bank, and the sensitive drugs and schemes were selected. The experiment was performed according to the procedure laid down in a previous report (17 (link)). The growth inhibition rates of different chemotherapeutic drugs were calculated in the laboratory, and test reports were prepared in the clinic. In contrast to the reference, the drug inhibition rates were classified as follows: high sensitivity (+++): inhibition rate ≥80%; moderate sensitivity (++): inhibition rate of 50%–80%; and low sensitivity (+): inhibition rate of 20%–<50%. After receiving the test report, the department selected the chemotherapy plan according to the test report. After the start of chemotherapy, the adverse effects on the patients were recorded. Three days after the end of the treatment course, the cardiac B-ultrasound, electrocardiogram, and biochemical indices were reexamined to observe the level of toxicity of the drug. The myelograms and peripheral blood routine were reexamined 14 days after the course of treatment; morphology, immunophenotype, cytogenetics, and molecular biology classification were performed, and the morphological characteristics of the cells were analyzed. When any signs of fungal infection were observed, antifungal drugs such as fluconazole/voriconazole and echinocandins were added to the treatment protocol.