Chemicals and reagents
MET306 (purity: 99.49%) was bought from Jiangsu Ascentage Pharma Development Co., Ltd. (Jiangsu, China), whereas the internal standard MET306-d6 (purity: 92.8%) was obtained from Chengdu Chempartner Co., Ltd. (Chengdu, China). All the drug and IS were stored at − 20 °C before use. High-performance liquid chromatography (HPLC) grade methanol and acetonitrile were provided by Merck Company (Darmstadt, Germany), and ultrapure water was provided by a Millipore Direct-Q® ultrapure water system (Billerica, MA, USA). Blank human plasma was supplied by the Phase I Clinical Trials Unit at the Second Affiliated Hospital of Zhejiang University School of Medicine (Hangzhou, Zhejiang, China). All other chemicals were of analytical grade, and they were used without further purification.
Instruments
An LC-30AD liquid chromatograph (Shimadzu, Japan) coupled with an AB SCIEXQTRAP® 5500 tandem mass spectrometer (ABSCIEX, USA) and an HTC-xt PAL autosampler (CTC, Switzerland) was used for analysis. The samples were weighed and prepared using an XP-26 balance (Mettler-Toledo International Inc.) and an AllegraX-15R centrifuge (Beckman Coulter Inc.), respectively.
Analytical conditions
Chromatographic separation was achieved using a Hypersil Gold-C18 (2.1 mm × 50 mm, 1.9 μm) column kept at 40 °C and a mobile phase consisting of a mixture of 5 mM ammonium acetate solution (A) and acetonitrile (B). The flow rate of the mobile phase was set at 0.3 mL min−1, and the gradient elution program was as follows: 0–0.5 min (5% B), 0.5–2 min (5–90% B), 0.2–3.5 min (90–90% B), and 3.51–4.5 min (5–5% B).
The AB SCIEXQTRAP® 5500 tandem mass spectrometer was operated in positive electrospray ionization (ESI) mode with multi-reaction monitoring (MRM), and the target ions for MET306 and the internal standard (IS) were m/z 466.2 → m/z 355.2 and m/z 472.2 → m/z 355.2, respectively. The flow rates of the Curtain Gas, Collision Gas, Ion Source Gas 1, and Ion Source Gas 2 were set at 40, 7, 20, and 60 L min−1, respectively. The temperature was adjusted at 550 °C, and the ion spray voltage was 5000 eV.
Stock solutions and working solutions
The stock solutions of the analytes and IS were prepared in methanol, at the concentration of 1 mg mL−1 for each compound. The MET306 solution was serially diluted with methanol to prepare 10, 40, 100, 400, 800, 2000, and 4000 ng mL−1 standard working solutions of the analyte, whereas the IS solution was diluted to 8 ng mL−1. The quality control working solutions of MET306 (3200, 320, 20, and 10 ng mL−1) were also prepared by dilution in methanol. All standard and quality control working solutions were stored at − 20 °C before analysis. The calibration curve (0.5–200 ng mL−1) and quality control samples of MET306 were prepared using blank plasma. The high- (HQC), medium- (MQC), and low-quality control (LQC) concentrations were 160, 16, and 1 ng mL−1, respectively, whereas the lower limit of quantitation (LLOQ) was 0.5 ng mL−1.
Sample extraction
MET306 was extracted from the plasma samples by protein precipitation. First, 50 μL of the collected plasma was pipetted into a 96-well plate along with 50 μL of the IS working solution (8 ng mL−1), after thawing. Then, 200 μL of acetonitrile was added to the mixture, followed by vortex mixing at 1200 rpm for 2.0 min. Subsequently, the mixture was centrifuged at 3000×g for 10 min, and the collected supernatant (10 μL) was injected into the LC–MS/MS for analysis.
Validation parameters
The analytical method used herein was validated according to the 2013 FDA BMV guidelines and the 2016 ICH M10 BMV draft guideline. The Analyst®1.5.2 was used for data interpretation of LC–MS data. Specifically, the selectivity, lower limit of quantification, linearity, intra- and inter-batch precision, accuracy, carryover, extraction recovery, matrix effect, and stability were assessed.
Calibration curve and selectivity
The calibration curve was constructed by analyzing blank plasma samples spiked with the MET306 standard. Each sample was analyzed three times over at least two days. The peak area ratios of MET306 to IS were plotted against analyte concentrations to obtain the calibration curve that was used to determine the concentration of MET306 in the analytical plasma samples. The validity of the standard calibration curve in the range of 0.5–200 ng mL−1 was assessed based on linear regression, with a 1/x weighting factor.
Blank plasma samples were used to assess the selectivity of the analytical method at the LLOQ. The LLOQ value of MET306 was determined based on the precision (%CV) and accuracy criteria of 20% and ± 20% of the nominal concentration, respectively. The peak areas of interferents should not be more than 20% of the LLOQ peak area and 5% of the IS peak area.
Precision and accuracy
The precision and accuracy of the method were assessed by analyzing the 0.5, 1, 16, and 160 ng mL−1 quality control samples six times. Intra-day precision and accuracy were measured on the same day, whereas the inter-day values were determined based on three runs conducted over at least two days.
The accuracy calculated at each concentration level should be within ± 15% of the nominal concentration (LLOQ should be within ± 20%), while the precision (%CV) should not exceed 15% (LLOQ should not exceed 20%).
Carryover
Carryover investigations were performed by running six sequential analyses of blank samples and MET306 solutions at LLOQ (0.5 ng mL−1) and ULOQ (200 ng mL−1) concentrations. The carryover was considered to be acceptable if the peak area of MET306 in the blank sample was less than 20% of the peak area in the LLOQ, and the peak area of the IS in the blank sample was less than 5% of the original peak area.
Matrix effect and recovery
To verify the potential suppression or enhancement of ionization, the matrix effect was assessed by analyzing the blank matrices of six individual donors at the concentrations of 1 and 160 ng mL−1. The matrix factors (MFs) of MET306 and IS were calculated as the ratios of the MET306 and IS peak areas detected in the presence of the matrix (measured by analyzing the blank plasma that had been spiked with the analyte after the extraction process) to those measured in the absence of it (pure solution), respectively. The IS-normalized MFs were obtained by dividing the MF values of MET306 by those of the IS. The CV % of the IS-normalized MFs corresponding to the six lots of matrices should be less than 15%.
The recovery of the method was determined by adding a known concentration of the analyte to the plasma before extraction and comparing the chromatographic peak areas of MET306 and IS in these samples to those obtained when the analyte was added after extraction. These analyses were repeated six times at each concentration (n = 6).
Stability
The stability of MET306 was evaluated by comparing the analyte peak areas of freshly prepared quality control samples to those of plasma samples (1 and 160 ng mL−1 quality control concentrations) kept at different temperatures for varying periods (n = 3). Specifically, the samples analyzed were MET306-spiked samples kept at room temperature for 6 h, ready-to-inject samples (after protein precipitation) kept in the HPLC autosampler at 4 °C for 24 h, and samples stored at − 70 °C for 386 days. The first two sets of samples were used to assess short-term stability, whereas the last set was used to determine long-term stability. The stability of the stock solution of MET306 (1 mg mL−1) and IS solution (8 ng mL−1) was evaluated similarly, and the freeze/thaw stability was examined after four complete freeze/thaw cycles (− 70 to 37 °C) conducted on consecutive days.
PK study
The PK study was carried out between April 2016 and December 2017 at the Phase I Clinical Trials Unit of the Second Affiliated Hospital of Zhejiang University School of Medicine (Hangzhou, Zhejiang, China). The Good Clinical Practices and ethical principles enunciated in the amended Declaration of Helsinki (revised version of Fortaleza, 2013) were strictly followed. The protocols and amendments of this study were reviewed and approved by the Human Subject Research Ethics Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine (Hangzhou, Zhejiang, China), and the study was registered at www.Chinadrugtrials.org.cn (CTR20160108 and CTR20160122).
The major inclusion criteria were as follows: (1) Willing to sign the informed consent; (2) age range from 18 to 75, regardless of gender; (3) histologically or cytologically confirmed NSCLC with stage IIIB to IV; (4) at any time since the initial diagnosis, there are clearly documented proven EGFR episodes associated with EGFR-TKI sensitivity Mutations; (5) the patient had at least one measurable lesion according to RECIST version 1.1, (6) ECOG strength score 0–1; (7) expected survival ≥ 3 months.
The major exclusion criteria were as follows: (1) The patient had been treated with EGFR-TKI; (2) patients received major surgery, chemoradiotherapy, immunotherapy, and other antitumor treatments that may interfere with the efficacy of the drug within 14 days before signing the informed consent; (3) patients who took adrenal steroid for more than two weeks in the first two weeks were screened; (4) pregnant or lactating women; (5) uncontrolled or active hepatitis B virus (HBV), hepatitis C virus (HCV) or HIV infection; (6) the researcher thinks it is not suitable to participate in this study.
The safety, tolerability, and pharmacokinetics of MET306 in patients with advanced NSCLC were assessed using a non-randomized, open-label, single, and multiple dosing escalation phase I clinical trial. In the first stage of this trial (single dosage stage), eligible patients were administered with a single dose of MET306 tablets and observed for 96 h before collecting the safety and tolerability data. In the second stage (multiple dosage stage), the patients were given an orally ingested tablet once a day for 14 consecutive days. In total, eight dosage groups of 10, 20, 30, 45, 60, 80, 105, 140 mg were investigated in this study.
Blood samples (2.5 mL each) were collected from each patient in K2EDTA anticoagulant tubes before MET306 administration and at 1, 2, 3, 4, 5, 6, 8, 10, 12, 24, 48, 72, and 96 h after single dosing. In the multiple dosage stage, the samples were collected on days 17, 18, and 19 before the ingestion of the drug tablet, and on day 19, they were collected at 1, 2, 3, 4, 5, 6, 8, 10, 12, 24, 48, 72, and 96 h after ingestion. The blood samples were centrifuged at 3000×g and 4 °C for 10 min, and after 60 min, the plasma-separated samples were stored at − 70 °C until analysis.
Metaheuristic optimization algorithms
Optimization algorithms are one of the efficient stochastic methods for solving optimization problems. In this study, the NGO algorithm is used, which simulates the behavior of the Northern Goshawk during prey hunting. This hunting strategy includes two stages of prey identification and tail-chasing processes (Dehghani et al. 2021).
The equation and parameter of initialization phase are as follows:
$$X = \left[ {\begin{array}{*{20}l} {X_{1} } \\ {\begin{array}{*{20}l} \vdots \\ {X_{i} } \\ \vdots \\ \end{array} } \\ {X_{N} } \\ \end{array} } \right]_{{N \times m}} = \left[ {\begin{array}{*{20}l} {X_{{1,1}} } \hfill & \ldots \hfill & {X_{{1,j}} } \hfill & \ldots \hfill & {X_{{1,m}} } \hfill \\ \vdots \hfill & \ddots \hfill & \vdots \hfill & {} \hfill & \vdots \hfill \\ {X_{{i,1}} } \hfill & \ldots \hfill & {X_{{i,l}} } \hfill & \ldots \hfill & {X_{{i,m}} } \hfill \\ \vdots \hfill & {} \hfill & \vdots \hfill & \ddots \hfill & \vdots \hfill \\ {X_{{N,1}} } \hfill & \ldots \hfill & {X_{{N,j}} } \hfill & \ldots \hfill & {X_{{N,m}} } \hfill \\ \end{array} } \right]_{{N \times m}}$$
$$F={\left[\begin{array}{c}{F}_{1}\\ \begin{array}{c}\vdots \\ {F}_{i}\\ \vdots \end{array}\\ {F}_{N}\end{array}\right]}_{N\times 1}={\left[\begin{array}{c}{F(X}_{1})\\ \begin{array}{c}\vdots \\ {F(X}_{i})\\ \vdots \end{array}\\ {F(X}_{N})\end{array}\right]}_{N\times 1}$$
$${X}_{i,j}={l}_{j}+rand\cdot \left({u}_{j}-{l}_{j}\right), i=\mathrm{1,2},\dots ,N,j=\mathrm{1,2},\dots ,m$$
where \(X\) represents the entire population of pelicans, each \({X}_{i}\) is a candidate solution to the given problem, and \(F\) stands for fitness function value. \({X}_{i,j}\) represents the value of the \(j\)th variable of the \(i\)th goshawk; \(N\) is the population size; \(m\) is the dimension; \(rand\) represents a random number between [0, 1]; \({l}_{j}\) represents the lower limit; \({u}_{j}\) represents the upper limit.
The equation and parameter of prey identification are as follows:
$${P}_{i}={X}_{k}, i=\mathrm{1,2},\dots ,N,k=\mathrm{1,2},\dots ,i-1,i+1,\dots ,N$$
$$x_{{i,j}}^{{new,p_{1} }} = \left\{ {\begin{array}{*{20}l} {x_{{i,j}} + r\left( {P_{{i,j}} - Ix_{{i,j}} } \right),~F_{{P_{i} }} < F_{i} } \\ {x_{{i,j}} + r\left( {x_{{i,j}} - P_{{i,j}} } \right),~F_{{P_{i} }} \ge F_{i} } \\ \end{array} } \right.$$
$$x_{i} = \left\{ {\begin{array}{*{20}l} {x_{i}^{{new,p_{1} }} ,} & {F_{i}^{{new,p_{1} }} < F_{i} } \\ {x_{i} ,} & {F_{i}^{{new,p_{1} }} \ge F_{i} } \\ \end{array} } \right.$$
where \(r\) is a random number belonging to [0,1]; \(I\) is a random number, which can be 1 or 2. When \(I\)=2, the displacement of each individual can be increased to make it enter a new area of the search space.
The equation of tail-chasing is as follows:
$${x}_{i,j}^{new,{p}_{2}}={X}_{i,j}+R\left(2r-1\right){X}_{i,j}$$
$$R=0.02\left(1-\frac{t}{T}\right)$$
$$x_{i} = \left\{ {\begin{array}{*{20}l} {x_{i}^{{new,p_{2} }} ,} & {F_{i}^{{new,p_{2} }} < F_{i} } \\ {x_{i} ,} & {F_{i}^{{new,p_{2} }} \ge F_{i} } \\ \end{array} } \right.$$
BPANN modeling strategy
The BPANN is a machine learning technology that uses artificial intelligence systems to simulate the cognitive process of biological nerves to the outside world. The algorithm consists of forward transfer of information and back propagation of error (Grunert et al. 2013; Noorizadeh et al. 2013). The specific steps of BPANN are as follows: (a) use particle swarm algorithm to determine the initial weight and threshold of the model. (b) The input information is forwarded from the input layer through the hidden layer to the output layer and the output of each layer of neurons acts on the input of the next layer of neurons. c) If the output layer does not get the expected output, calculate the error change value of the output layer, propagate the error signal back along the original connection path through the network, and then modify the weights of each layer until the desired goal is achieved. The BPANN model can theoretically approximate any continuous function with arbitrary precision (Mei et al. 2019; Jun et al. 2020).
In this study, the groups of 10, 20, 30, 45, 60, 80 were used as development data to construct the NGO-BPANN model, and the groups of 105, 140 mg not involved in modeling were used as testing data to test the NGO-BPANN model.