An Ultra-Fast Green UHPLC-MS/MS Method for Assessing the In Vitro Metabolic Stability of Dovitinib: In Silico Study for Absorption, Distribution, Metabolism, Excretion, Metabolic Lability, and DEREK Alerts
Abstract
1. Introduction
2. Materials and Methods
2.1. Materials
2.2. Instruments
2.3. Assessment of DVB Metabolic Lability
2.4. Screening of the DVB Toxicity Alerts Using DEREK In Silico Software
2.5. DVB In Silico ADME Profile
2.6. UHPLC-MS/MS Analytical Features
2.7. DVB and EFB Working Dilutions
2.8. DVB Calibration Levels
2.9. The Extraction Recovery of DVB and EFB
2.10. Validation Features of the Established UHPLC-MS/MS Approach
2.10.1. Specificity
2.10.2. Sensitivity and Linearity
2.10.3. Accuracy and Precision
2.10.4. Extraction Recovery and Matrix Effect
2.10.5. Stability
2.11. Evaluation of the System Greenness Employing AGREE Software
2.12. In Vitro Assessment of the DVB Metabolic Stability
3. Results
3.1. In Silico Metabolic Lability Assessment of DVB
3.2. In Silico Testing of DVB Toxicity Alerts Using DEREK Module
3.3. In Silico ADME Parameters
3.4. UHPLC-MS/MS Approach
3.5. Validation of the LC-MS/MS Approach
3.5.1. Specificity
3.5.2. Linearity and Sensitivity
3.5.3. Accuracy and Precision Validation Parameters
3.5.4. The HLMs Matrix Does Not Show Any Observed Effect on the Extraction Recovery of DVB in the Proposed UHPLC-MS/MS System
3.5.5. Stability of DVB in the HLMs (The Metabolic Incubation Matrix) and DMSO
3.6. Evaluation of the UHPLC-MS/MS System Greenness Utilizing the AGREE Software
3.7. In Vitro Metabolic Incubations of DVB with HLMs
4. Discussions
5. Limitation of the Current Study
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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UPLC | TQD MS | ||||
---|---|---|---|---|---|
Eclipse plus-C8 reversed column | Particle size | 3.5 μm | ESI | Positive ESI source | |
Internal diameter | 2.1 mm | The extractor voltage | 3.0 (V) | ||
Length | 50.0 mm | Cone gas rate | 100 L/hr | ||
Column T | 22.0 ± 2.0 °C | The RF lens voltage | 0.1 (V) | ||
Isocratic mobile phase system | Aqueous part | 0.1% Formic acid in H2O | Nitrogen gas | Drying gas | |
55% | 100 L/hr | ||||
pH: 3.2 | 350 °C | ||||
Organic part | 45% ACN | Capillary voltage: 4 KV | |||
Injection volume: | 5.0 μL | Argon gas | 0.14 mL/min | ||
Flow rate | 0.5 mL/min. | Mode | MRM |
Time Segment | Rt | Mass Transitions (m/z) | |
---|---|---|---|
MRM detection segments | 0.0 to 0.65 min | DVB (0.44 min) | 393→336 (CE a:32 and CV b: 28) |
393→58 (CE:36 and CV: 46) | |||
0.65 to 1.0 min | EFB (IS; 0.77 min) | 540→359 (CE: 56 and CV: 36) | |
540→116 (CE: 56 and CV: 32) |
Physicochemical Properties | Water Solubility | ||
---|---|---|---|
Formula | C21H21FN6O | Solubility | 8.60 × 10−2 mg/mL; 2.19 × 10−4 mol/L |
Molecular weight | 392.43 g/mol | Log S (ESOL) | −3.66 |
Heavy atoms num. | 29 | Class | Soluble |
Arom. heavy atoms num. | 19 | Solubility | 2.32 × 10−1 mg/mL; 5.92 × 10−4 mol/L |
Rotatable bonds num. | 2 | Log S (Ali) | −3.64 |
Fraction Csp3 | 0.24 | Class | Soluble |
Solubility | 8.66 × 10−5 mg/mL; 2.21 × 10−7 mol/L | ||
Num. H-bond donors | 3 | Log S (SILICOS-IT) | −6.66 |
TPSA | 94.04 Å2 | Class | Poorly soluble |
Num. H-bond acceptors | 4 | Medicinal Chemistry | |
Molar refractivity | 120.28 | Brenk | 0 alert |
Lipophilicity features | PAINS | 0 alert | |
Log Po/w (XLOGP3) | 1.64 | Leadlikeness | No; 1 violations: MW > 350 |
Log Po/w (iLOGP) | 2.26 | Synthetic accessibility | 3.20 |
Log Po/w (MLOGP) | 2.31 | Pharmacokinetics | |
Log Po/w (SILICOS-IT) | 3.27 | GI absorption | High |
Log Po/w (WLOGP) | 2.21 | P-gp substrate | Yes |
Consensus Log Po/w | 2.34 | Permeant to BBB | No |
Druglikeness features | Inhibiton of CYP1A2 | Yes | |
Ghose | Yes | Inhibiton of CYP2D6 | Yes |
Lipinski | Yes; 0 violation | Inhibiton of CYP3A4 | No |
Egan | Yes | Inhibiton of CYP2C9 | No |
Veber | Yes | Inhibiton of CYP2C19 | No |
Muegge | Yes | Skin permeation (Log Kp) | −7.53 cm/s |
The score of bioavailability | 0.55 |
Analytes | Recovery | Stationary System | Stationary System | |||
---|---|---|---|---|---|---|
Solid Phase Extraction | Protein Precipitation Using ACN | Methanol | ACN | C18 Column | C8 Column | |
DVB | Low (88.27%) | High (102.62 ± 3.73%) | 0.52 min | 0.43 min | 0.74 min | 0.43 min |
Not precise | Precise (RSD < 3.63%) | Tailed | Good peak | Tailed peaks | Perfect shape | |
EFB | Good (89.89%) | High (101.61 ± 3.23% | 0.87 min | 0.77 min | 1.25 min | 0.77 min |
Not precise | Precise (RSD < 3.18%) | Overlapped | Optimum peak shape | Perfect shape | Optimum shape |
DVB (ng/mL) | Mean | SD | Accuracy (%) | RSD (%) | Recovery |
---|---|---|---|---|---|
1.00 | 1.07 | 0.08 | 7.18 | 7.28 | 107.18 |
15.00 | 14.73 | 0.09 | −1.83 | 0.64 | 98.17 |
40.00 | 40.93 | 0.28 | 2.32 | 0.69 | 102.32 |
100.00 | 100.96 | 1.57 | 0.96 | 1.56 | 100.96 |
250.00 | 254.41 | 1.90 | 1.77 | 0.75 | 101.77 |
500.00 | 508.02 | 5.97 | 1.60 | 1.18 | 101.60 |
2000.00 | 1966.90 | 21.08 | −1.65 | 1.07 | 98.35 |
3000.00 | 2997.38 | 13.60 | −0.09 | 0.45 | 99.91 |
% Recovery | 101.28 ± 2.84 |
DVB (ng/mL) | Intra-Day (12 Sets in 1 Day) | Inter-Day (6 Sets in 3 Days) | ||||||
---|---|---|---|---|---|---|---|---|
QCs | 1.00 | 3.00 | 900.00 | 2400.00 | 1.00 | 3.00 | 900.00 | 2400.00 |
Mean | 1.07 | 3.10 | 902.61 | 2406.71 | 1.09 | 3.05 | 894.99 | 2389.57 |
SD | 0.08 | 0.03 | 7.54 | 10.10 | 0.03 | 0.06 | 5.18 | 5.74 |
Precision (%RSD) | 7.28 | 0.81 | 0.83 | 0.42 | 2.30 | 2.00 | 0.58 | 0.24 |
% Accuracy | 7.18 | 3.19 | 0.29 | 0.28 | 9.33 | 1.68 | −0.56 | −0.43 |
Recovery (%) | 107.18 | 103.19 | 100.29 | 100.28 | 109.33 | 101.68 | 99.44 | 99.57 |
Stability as Validation Features | Mean | SD | Precision (%RSD) | Accuracy (%E) | ||||
---|---|---|---|---|---|---|---|---|
3.0 | 2400.0 | 3.0 | 2400.0 | 3.0 | 2400.0 | 3.0 | 2400.0 | |
Long-Term (−80 °C for 28 d) | 2.91 | 2388.44 | 0.05 | 5.52 | 1.82 | 0.23 | −3.00 | −0.48 |
Auto-Sampler (15 °C for 24 h) | 3.01 | 2406.68 | 0.12 | 7.77 | 4.13 | 0.32 | 0.22 | 0.28 |
Freeze–Thaw (3 cycles at −80 °C) | 3.06 | 2414.45 | 0.08 | 14.51 | 2.72 | 0.60 | 2.11 | 0.60 |
Short-Term (4 h at room T) | 2.95 | 2390.13 | 0.11 | 6.38 | 3.59 | 0.27 | −1.56 | −0.41 |
Principles | Score | Weight |
---|---|---|
1. To avoid the necessity for sample treatment, it is recommended to use direct analytical methods. | 0.3 | 2 |
2. The targets of this research are to achieve a minimal amount and a small sample size of specimens. | 0.75 | 3 |
3. Ideally, it is advisable to perform assessments on-site whenever it is feasible. | 0.66 | 2 |
4. Studies have shown that the integration of analytical steps and activities leads to positive results concerning energy preservation and reducing the use of reagents. | 1.0 | 3 |
5. It is advisable to choose automated and streamlined operations. | 0.75 | 2 |
6. Avoiding the implementation of derivatization procedures is prudent. | 1.0 | 2 |
7. To reduce the causing of a noteworthy amount of analytical surplus and implement effective disposal methods is paramount. | 0.88 | 2 |
8. Within the field of analytical chemistry, there is a predilection for employing multi-analyte or multi-parameter methodologies as opposed to those that just concentrate on a single analyte. | 1.0 | 2 |
9. Attempts should be considered to reduce energy use. | 0.0 | 2 |
10. It is sensible to give importance to the usage of reagents produced from renewable sources. | 0.5 | 1 |
11. The necessity of eliminating or replacing detrimental substances is of paramount significance. | 1.0 | 2 |
12. There is a necessity to improve the safety regulations for workers. | 1.0 | 2 |
Time (min.) | Mean a (ng/mL) | X b | LN X | The Linear Segment Features |
---|---|---|---|---|
0.00 | 380.45 | 100 | 4.61 | Regression line equation: y = −0.04479x + 4.563 |
2.50 | 321.44 | 84.49 | 4.44 | |
5.00 | 279.33 | 73.42 | 4.30 | R2 = 0.9920 |
7.50 | 252.96 | 66.49 | 4.20 | |
15.00 | 195.86 | 51.48 | 3.94 | Slope: −0.04479 |
20.00 | 143.62 | 37.75 | 3.63 | |
25.00 | 120.75 | 31.74 | 3.46 | t1/2: 15.48 min and |
30.00 | 101.88 | 26.78 | 3.29 | Clint: 52.39 mL/min/kg |
45.00 | 94.28 | 24.78 | 3.21 | |
60.00 | 71.30 | 18.74 | 2.93 |
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Attwa, M.W.; Abdelhameed, A.S.; Kadi, A.A. An Ultra-Fast Green UHPLC-MS/MS Method for Assessing the In Vitro Metabolic Stability of Dovitinib: In Silico Study for Absorption, Distribution, Metabolism, Excretion, Metabolic Lability, and DEREK Alerts. Medicina 2024, 60, 1626. https://doi.org/10.3390/medicina60101626
Attwa MW, Abdelhameed AS, Kadi AA. An Ultra-Fast Green UHPLC-MS/MS Method for Assessing the In Vitro Metabolic Stability of Dovitinib: In Silico Study for Absorption, Distribution, Metabolism, Excretion, Metabolic Lability, and DEREK Alerts. Medicina. 2024; 60(10):1626. https://doi.org/10.3390/medicina60101626
Chicago/Turabian StyleAttwa, Mohamed W., Ali S. Abdelhameed, and Adnan A. Kadi. 2024. "An Ultra-Fast Green UHPLC-MS/MS Method for Assessing the In Vitro Metabolic Stability of Dovitinib: In Silico Study for Absorption, Distribution, Metabolism, Excretion, Metabolic Lability, and DEREK Alerts" Medicina 60, no. 10: 1626. https://doi.org/10.3390/medicina60101626
APA StyleAttwa, M. W., Abdelhameed, A. S., & Kadi, A. A. (2024). An Ultra-Fast Green UHPLC-MS/MS Method for Assessing the In Vitro Metabolic Stability of Dovitinib: In Silico Study for Absorption, Distribution, Metabolism, Excretion, Metabolic Lability, and DEREK Alerts. Medicina, 60(10), 1626. https://doi.org/10.3390/medicina60101626