
==== Front
J Taibah Univ Med Sci
J Taibah Univ Med Sci
Journal of Taibah University Medical Sciences
1658-3612
Taibah University

S1658-3612(24)00066-0
10.1016/j.jtumed.2024.07.004
Original Article
QSAR, molecular docking, and pharmacokinetic analysis of thiosemicarbazone-indole compounds targeting prostate cancer cells
Kubo Abdulrahman Ibrahim M.Sc abdulrahmankuboibrahim@gmail.com
ac⁎
Uzairu Adamu PhD b
Babalola Ibrahim Tijjani PhD a
Ibrahim Muhammad Tukur PhD b
Umar Abdullahi Bello PhD b
a Department of Chemistry, Faculty of Science, Yobe State University, Damaturu, Nigeria
b Department of Chemistry, Faculty of Physical Science, Ahmadu Bello University, Zaria, Nigeria
c Department of Pure and Applied Chemistry, Faculty of Science, Adamawa State University, Mubi, Nigeria
⁎ Corresponding address: Department of Chemistry, Faculty of Science, Yobe State University, Damaturu, Nigeria. abdulrahmankuboibrahim@gmail.com
01 8 2024
8 2024
01 8 2024
19 4 823834
23 5 2023
23 5 2024
24 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objectives

By 2030, prostate cancer is estimated to account for 1.7 million new cases and 499,000 deaths. The objectives of this research were to create a model revealing the activity of thiosemicarbazone-indole compounds as anticancer agents against the PC3 cell line; perform docking analysis between the compounds and the target enzyme; and predict the pharmacokinetics and drug-likeness of the compounds under investigation.

Methods

The quantitative structureactivity relationship (QSAR) method was used to build the model; molecular docking between the compounds and the target enzyme was performed; and the drug-likeness and pharmacokinetics of the inhibiting compounds was examined.

Results

The genetic function algorithm-multilinear regression approach was used for building the QSAR model. Build model 1 had the best performance, with R2 (coefficient of determination) = 0.972517, Radj (adjusted R-squared) = 0.964665, (CRp2) = 0.780922, and LOF (leave-one-out cross-validation) = 0.076524, demonstrated strongly indicated by the molecular descriptors. SHBd, SsCH3, JGI2, and RDF60P were highly dependent on proliferative activity. Compounds ID 7 and 22 had the potential to act as androgen receptor inhibitors, as suggested by molecular docking studies between the drugs and their target enzymes. Compounds ID 7 and 22 exhibited binding scores of −8.5 kcal/mol and −8.8 kcal/mol, respectively. The approved maximum medication molecules for oral bioavailability included the molecules with IDs 7 and 22.

Conclusion

This research provides valuable insights into the relationships among molecular descriptors, potential inhibitors, and pharmacokinetic properties in the treatment of PC3. These findings may contribute to the understanding and potential development of new therapeutic options for prostate cancer patients.

الملخص

أهداف البحث

بحلول عام 2030، من المتوقع أن يتسبب سرطان البروستاتا في 1.7 مليون حالة جديدة و499 ألف حالة وفاة. أهداف هذا البحث هي إنشاء نموذج يربط بين تصرفات ثيوسيميكاربازون-إندول كعامل مضاد للسرطان ضد خط خلايا بي سي 3، وإجراء تحليل الالتحام بين المركبات والإنزيم المستهدف، والتنبؤ بالحركية الدوائية والتشابه الدوائي للمركبات قيد التحقيق.

طريقة البحث

استخدمت الطريقة العلاقة الكمية بين البنية والنشاط لبناء النموذج، وأجرت الالتحام الجزيئي بين المركبات والإنزيم المستهدف، وفحصت تشابهها مع الأدوية وتحليل الحرائك الدوائية للمركبات المثبطة.

النتائج

تم استخدام منهج الانحدار متعدد الخطوط لخوارزمية الوظيفة الجينية في بناء نموذج العلاقة بين الهيكل الكمي والنشاط. المعلمات التالية من نموذج البناء الأول، كأفضل، آر2 (معامل التحديد) = 0.972517، "رادج" (المعدل آر- التربيعي) = 0.964665، "سي آر بي 2" = 0,780922، و "إل أو إف" (التحقق من صحة التقاطع لمرة واحدة) = 0.076524، ظهر بقوة على الواصفات الجزيئية. كانت "إس إتش بي دي" و "إس إس سي إتش 3" و "جاي جي آي 2" و "أر دي إف 60 بي" تعتمد بشكل كبير على النشاط التكاثري. تتمتع المركبات ذات المعرفين 7 و22 بالقدرة على العمل كمثبطات لمستقبلات الأندروجين، كما اقترحت دراسات الالتحام الجزيئي بين الأدوية والإنزيمات المستهدفة. تظهر المركبات ذات المعرفين 7 و22 قيد التحقيق درجات ربط تبلغ -8.5 سعرة حرارية/مول و-8.8 سعرة حرارية/مول، على التوالي. كانت الجزيئات ذات المعرفين 7 و22 ضمن الحد الأقصى المقبول لجزيئات الدواء لتكون متاحة بيولوجيا عن طريق الفم.

الاستنتاجات

يقدم هذا البحث رؤى قيمة حول العلاقة بين الواصفات الجزيئية والمثبطات المحتملة والخصائص الدوائية في علاج بي سي 3. تساهم هذه النتائج في فهم الخيارات العلاجية الجديدة لمرضى سرطان البروستاتا وتطويرها المحتمل.

الكلمات المفتاحية

الحرائك
الجزيئي الالتحام
الخلايا خط
لسيليكو في
البروستاتا سرطان
الدوائي التشابه
الدوائية سي ي
Keywords

In silico
Molecular docking
(PC3) cell line
Pharmacokinetics
Prostate cancer
QSAR
==== Body
pmcIntroduction

The continuing emergence of novel diseases, coupled with the diminishing effectiveness of existing treatments, underscores the urgent need for innovative solutions. This need for new treatments has led to increased exploration of diverse resources, particularly plants and microorganisms.1,2 The conventional trial-and-error approach to drug design is costly, environmentally disruptive, and time-consuming.3 Consequently, computational and theoretical chemistry methods, such as quantitative structure–activity relationship (QSAR) and structure–activity relationship, have substantially advanced the understanding of metabolism of new drugs in early development stages.4

One groundbreaking advancement in drug design has been the development of computational chemistry and molecular modeling approaches. These tools have become indispensable for the discovery, optimization, and design of novel drug candidates.4,5 According to GLOBOCAN (2018), 1,276,106 new cases of prostate cancer were recorded worldwide. This cancer is more common in developed countries, which have shown a death rate of 358,989. With increases in the global population, prostate cancer is expected to reach 1.7 million new cases and 499,000 deaths by the year 2030.6, 7, 8 Current therapies such as chemotherapy, radiation therapy, and surgery have become ineffective because of severe adverse effects and multidrug resistance.9 Despite the abundance of medications on the market, their clinical effectiveness remains insufficient.10 Therefore, this chronic illness is considered a major issue requiring prompt pharmaceutical treatment.11

Nature is the primary source of several cures for various illness.12 In this context, the present study focused on the exploration of thiosemicarbazone derivatives, which exhibit promising biological activity.13 These compounds are rich in sulfur and nitrogen, and have diverse biological and therapeutic properties.14 Notably, thiosemicarbazones have gained attention for their anticancer potential, thus prompting computational investigations into their pharmacological activity.15

Thiosemicarbazones have demonstrated remarkable potential in medicinal chemistry, in applications including pharmaceutical, bacterial, and material synthesis.15 Their ability to bind transition metals has prompted interest in catalysis and medicinal applications.16,17 The compounds are recognized for their antiproliferative effects and are considered potential candidates for anticancer drugs.18, 19, 20 Given their demonstrated biological activity, including antituberculosis, antiviral, antifungal, antimalarial, and, notably, antineoplastic actions, thiosemicarbazones have become pharmacophores of interest to chemists and biologists.18,21, 22, 23, 24, 25 Several derivatives, including thiosemicarbazone and its derivatives, are currently undergoing investigation in phase I and phase II clinical trials against various cancers.26, 27, 28 The search for potent and safer anticancer compounds remains a critical focus of contemporary cancer research.29

Indole scaffolds are known to avert the multiplication and invasion of many cancer cells.30 Moreover, indole derivatives, because of their pharmacological attributes, have emerged as a promising research field and have piqued the interest of researchers.31, 32, 33 These derivatives are widely used as synthons for the preparation of a wide variety of biologically important heterocycles.34 Additionally, indole is present in important synthetic therapeutic compounds such as anti-HIV35 and anti-cancer36 drugs.

In prostate cancer research, the PC3 cell line is particularly prominent among the three commonly used prostate cancer cell lines, PC3, DU145, and LNCaP. Therefore, investigating the activity of thiosemicarbazone derivatives against PC3 prostate cancer cell lines is of substantial relevance.

To facilitate drug candidate discovery, a systematic and robust approach is essential. This study used QSAR and molecular docking techniques to predict the activity of thiosemicarbazone derivatives against PC3 prostate cancer cell lines. Our aim was to unravel the intricate relationship between these compounds and their receptor, to gain a deeper understanding of their mechanism of action.

Materials and Methods

Sourcing of data

A series of thiosemicarbazone-indole derivatives with antiproliferative activity (IC50) against the PC3 cancer cell line were identified from the literature.37 Antiproliferative activity (IC50), measured in micromolar concentrations (m), was transformed to a logarithmic scale (pIC50), and the negative base 10 logarithm of all compounds was determined with equation (1).(1) pIC50 = −log10 (IC50 × 10−6)

Drawing of 2D-molecular structures and geometric optimization

ChemDraw v12.0 software was used to draw the 2D structures of all molecules in the data set. The 2D structures of all molecules were converted to 3D in Spartan 14.1.1.0v software. Energy minimization was performed to decrease structural constraints before the stable conformation of the molecules in terms of possible energy was determined.38 The Bee-3-Lee Yang per method of optimization, with density functional theory calculations in the 6-31G∗ Basic set in Spartan 14.1.1.0v software, were employed to ascertain the connections' geometrical structure. Spartan 14.1.1.0v software was used to perform optimization aimed at positioning the stable structures of all molecules at the universal minimum on the potential energy surface.38

Model development and validation

The generated model was evaluated with Friedman's formula,39 as follows:(2) LOF=SEE(1–c+dP)2M

where Friedman lack of fit (LOF) is the estimated robustness of a model, SEE is the standard error of estimation, P is the total number of descriptions in the model, d is the user-defined smoothing parameter, C is the number of terms in the model, and M is the number of compounds in the training set.

SEE was determined as follows:(3) SEE=(Yexp−Yprd)N−P−1

where Yexp, Ypred are the verified and calculated pIC50 values of the modeling set samples, N is the number of samples in the modeling data set, and P is the number of independent variables present in the generated model.40

The correlation coefficient R2 of the build model was another parameter considered; values closer to 1.0 indicated a better built model. R2 is represented as:(4) R2=1−Ʃ(Yexp−Yprd)2Ʃ(Yexp−Ytrn)2

where Yprd, Yexp, and Ytrn are the predicted, experimental, and average experimental activity in the training set, respectively.

The strength of the model did not depend on the value of R2, because the value of R2 was directly proportional to the number of descriptors in the model. Therefore, for an authentic and robust model, R2 was modified accordingly.(5) Radj2=(n−1)(R2−P)n−P−1

where P is the number of descriptors in the model, and n is the number of compounds used in the training set. The cross-validation coefficient, Qcv2 was as follows:(6) Qcv2=1−∑(Yprd–Yexp)2∑(Yexp–Ymtrn)2

where Yprd, Yexp, and Ymtrn are the anticipated, investigational, and standard experimental activity in the training set, respectively.

A test set was used to externally validate the generated model by assessing the value of Rpred2, as follows:(7) Rprd2=1−∑(Yprd−Yexp)2∑(Yexp−Ymtrn)2

where Yprd and Yexp are the predicted experimental and average experimental activity of the test set, respectively, and Ymtrn is the average experimental activity of the training set.41

Y-randomization test

Random multiple linear regression models were generated with a training set of Y-randomized tests. In this case, the R2 and Q2 values were required to be low for the QSAR model to be constructed.41 Additionally, the coefficient of determination cR2p was required to exceed 0.5 to pass this test and was also calculated in the Y-randomization test, as follows:(8) cR2p = Rx (R2 − R2r)2

SwissADME

SwissADME, an online tool available to predict pharmacokinetics, drug-likeness, physiochemical properties, and medicinal chemistry,42 was used to estimate the potency was estimated based on their in silico characteristics, and we were asked to modify these compound, CPY1A2, CPY2C19, CPY2C9, CPY2D6, CPY34A, and Lipinski's rule of five.43

Molecular docking studies of the investigated compound against the PC3 receptor

The optimized compounds underwent molecular docking with the androgen receptor 5T8E (Figure 1) downloaded from the Protein Data Bank and were prepared in Discovery Studio software. The ligands were also transformed to PDB format. Pyrex docking software was used to calculate the binding affinities of the ligands and receptors.44 The target receptor may play a role in remodeling the effectiveness and strength of the recommended compounds as potential cancer drugs.Figure 1 Crystal structure of the prepared androgen receptor (PDB ID: 5T8E).

Figure 1

Results and discussion

In silico methods are computational approaches used to obtain and optimize potential drug candidates. QSAR models the activity of various compounds as a linear combination of specific molecular descriptors. A molecular descriptor is a numerical value representing a particular molecular property of a compound.45,46 A robust QSAR model uses molecular descriptors that significantly influence the activity of the compounds and can be used to predict the activity of other similar compounds.47

In this study, a QSAR model was constructed to predict the antiproliferative activity of thiosemicarbazone-indole derivatives. The Kennard-Stones algorithm was used in Dataset Division GUI v1.2 software to split the data into test and training sets. The training set was used to construct the model, whereas the test set was used to validate the model. Using the genetic function algorithm, we constructed five distinct QSAR models. Model 1 had the best performance, according to its statistical fitness. The selected QSAR model was powerful and predictable, with R2 (coefficient of determination) = 0.972517, Radj (adjusted r-squared) = 0.964665, cRp2 = 0.780922, and LOF (leave-one-out cross-validation) = 0.076524, respectively.Model 1:

(9) Y = −1.0308891697∗SHBd + 0.407863672∗SsCH3 − 115.07794375∗JGI2 − 0.150532229∗RDF60p

The biological, computed, and residual values of thiosemicarbazone-indole compounds are presented in Table 1. The low residual values, derived from the difference between biological and computed activity, displayed high predictive power in equation (1) (residual = biological activity − computed activity). Descriptors from model 1 are interpreted and displayed in Table 2, and each molecular descriptor significantly contributed to predicting compound activity.Table 1 Verified and calculated pIC50 values of thiosemicarbazone-indole series against the PC3 cancer cell line.

Table 1S/No	Compounds	pIC50	Predicted IC50	Residual	Docking score (kcal/mol)	
1.	Image 1	6.4244	6.6179	−0.1935	−6.2	
2.a	Image 2	5.6531	5.7726	0.1195	−7.7	
3.	Image 3	6.2907	6.3862	0.955	−8.3	
4.	Image 4	5.8465	5.9383	0.0918	−7.9	
5.	Image 5	6.6179	6.7237	0.1058	−8.1	
6.	Image 6	7.0915	7.1495	0.058	−8.1	
7.a	Image 7	6.9172	6.4973	−0.4199	−8.5	
8.	Image 8	7.2146	7.1707	−0.0439	−8.4	
9.	Image 9	7.2676	7.2529	−0.0147	−7.4	
10.	Image 10	5.6047	5.5104	−0.0942	−7.7	
11.a	Image 11	6.8762	6.7675	−0.1087	−8.1	
12.	Image 12	7.0315	7.2046	01731	−8.3	
13.	Image 13	7.0409	6.8889	−0.152	−7.7	
14.	Image 14	5.8236	5.9321	0.1085	−7.5	
15.	Image 15	7.0757	7.0517	−0.024	−8.1	
16.	Image 16	5.7956	5.9655	0.1699	−7.9	
17.	Image 17	6.3429	6.3676	0.0247	−7.4	
18.a	Image 18	6.4922	6.1244	−0.3678	−8.2	
19.	Image 19	6.1506	6.1608	0.0102	−8.2	
20.	Image 20	6.0236	6.0866	0.063	−8.4	
21.	Image 21	7.2676	7.0765	−0.1911	−7.9	
22.	Image 22	4.7698	4.6825	−0.0873	−8.8	
23.a	Image 23	5.2740	4.9504	−0.3236	−8.0	
24.	Image 24	6.3915	6.2922	−0.0993	−8.4	
a Denotes test set.

Table 2 Definition and class of molecular description in the build model.

Table 2Name	Definition	Class	
SHBd	Sum of estate for (strong) hydrogen bond donors	2D	
SsCH3	Sum of atom-type E state: CH3	2D	
JGI2	Mean topological charge index of order 2	2D	
RDF60p	Radial distribution function 060/weighted by relative polarizability	3D	

The average effect of the model 1 parameters revealed that SsCH3 had a positive coefficient; therefore, an increase in this factor would elevate the bioactivity of these derivatives. In contrast, SHBd, JGI2, and RDF60p had negative coefficients; therefore, a decrease in these descriptors would increase the experimental activity of thiosemicarbazone-indole compounds. These findings highlight the contribution of each descriptor to predicting the response activity.

Table 2 displays the 2D and 3D descriptors of these models, including SHBd, SsCH3, JGI2, and RDF60p. For instance, SHBd is a 2D sum of estate for the (strong) hydrogen bond donor, SsCH3 is a 2D sum of atom-type E states: CH3 and JGI2 represent the 2D average topological charge exponents of order 2, and RDF0p is the 3D radial distribution function, weighted by 060/relative polarizability.

Table 3 provide the accepted QSAR validation tool and the minimum required values for evaluating the model.48 The accuracy of the equation was assessed according to the pIC50 values of the calibration compounds and the validity of compounds.Table 3 QSAR validation tool.

Table 3Validation tool	Interpretation	Accepted value	
R2	Coefficient of determination	≥0.6	
Rcv2	Cross validation coefficient	>0.5	
Radj2	Adjusted coefficient of determination	>0.5	
R2 − Qcv2	Difference between R2 and Qcv2	≤0.03	
Next/test set	Minimum number of external test set	≥5	
R2test set	Coefficient of determination of external and test set	≥0.5	

To confirm the stability, reliability, and robustness of the built QSAR model, we conducted Y-randomization tests. The results of multiple trials for R2 and Q2 values are presented in Table 4. A cR²p value above 0.5 signifies that the model has a good fit and is capable of making accurate predictions, reflecting the model's robustness and reliability in predictive tasks. Table 5 shows the statistical parameters of the built models. Additionally, a graph of calculated pIC50 values versus biological pIC50 values was plotted to illustrate the relationships among the derivatives in Figure 2. Figure 3 depicts a plot of experimental activity versus standardized residuals for the derivatives.Table 4 Y-randomization test.

Table 4Model	R	R2	Q2	
Original	0.914764	0.836794	0.754947	
Random 1	0.282576	0.079849	−0.37938	
Random 2	0.274074	0.075117	−0.5798	
Random 3	0.224871	0.050567	−1.49993	
Random 4	0.248583	0.061793	−0.79437	
Random 5	0.415275	0.172453	−0.21632	
Random 6	0.336942	0.11353	−0.85686	
Random 7	0.444572	0.197644	−0.1481	
Random 8	0.32334	0.104549	−0.42676	
Random 9	0.176534	0.031164	−0.42356	
Random 10	0.559751	0.313321	−0.14548	
Random model parameters	
Average r:	0.328652	
Average r2:	0.119999	
Average Q2:	−0.54706	
CRp2:	0.780922	

Table 5 Statistical parameters for the developed models.

Table 5Parameter	Model 1	Model 2	Model 3	Model 4	Model 5	
Friedman LOF	0.076524	0.085553	0.104593	0.104676	0.105337	
R-squared	0.972517	0.969275	0.962437	0.962407	0.962169	
Adjusted R-squared	0.964665	0.960496	0.951705	0.951666	0.951361	
Significant regression	YES	YES	YES	YES	YES	
Significance-of-regression F-value	123.8526	110.4132	89.67639	89.60198	89.01778	
Critical SOR F-value (95%)	3.160163	3.160163	3.160163	3.160163	3.160163	
Replicate points	0	0	0	0	0	
Computed experimental error	0	0	0	0	0	
Lack-of-fit points	14	14	14	14	14	
Minimum experimental error for non-significant LOF (95%)	0.099165	0.104852	0.115934	0.11598	0.116346	

Figure 2 Scatter plot of biological activity against calculated activity.

Figure 2

Figure 3 Scatter plot of standardized residual versus investigational activity.

Figure 3

Docking results

In silico molecular docking methods can be used to examine the binding relationship between a ligand and receptor. Macromolecules known as receptors are typically found in tissues, biological receptors, and enzymes. The primary focus of molecular docking is on the type of interaction between the receptor and the ligands, as well as the binding affinity or energy.49,50 In silico compounds are created with structure-based techniques, and the results of molecular investigations are used. The investigated compounds (thiosemicarbazone-indole) targeting PC3 cell lines underwent docking studies with the protein target (PDB ID: 5T8E). The binding scores, representing the affinity of a compound to its receptor, and indicating the robustness of the interaction, are displayed in Table 1.

The relationship between compounds 22 and 7 and the androgen receptor, including binding affinity, is described in Table 6, which shows the nature of their interactions and the amino acid residues involved in interacting with the receptor. Because of their docking scores of −8.8 and −8.5 kcal/mol, respectively, compounds 22 and 7 were chosen for these experiments, and demonstrated robust contact between the docking ligand and the receptor. The most common interactions among the chosen ligands were alkyl and pi-alkyl, although the range of bonding interactions also included Van der Waals, conventional hydrogen bonds, and carbon hydrogen bonds. According to the molecular docking results, the most frequent amino acid residues for all studied compounds were VAL, ALA, LYS, GLY, PRO, TRY, ARG, GLU, and GLN (Table 6).Table 6 Molecular docking interactions in select compounds.

Table 6Compound	Binding affinity (kcal/mol)	Amino acid	Interaction	
22	−8.8 kcal/mol	VAL A:715, ALA A:748, LYS A:808	Van der Waals, salt bridge,	
	GLY A:683, PRO A: 682, TRY A:763	attractive charge,	
	PRO A:766, VAL A:684, ARG A:752	conventional hydrogen bond,	
	PHE A:804, GLU A:681, TRP A:751	carbon hydrogen bond,	
	GLN A:711	unfavorable positive-positive,	
		pi-sulfur, alkyl, pi-alkyl	


	
7	−8.5 kcal/mol	PRO A: 766, TYR A:763, ASN A:756	Van der Waals, pi-sigma,	
	GLU A:681, ARG A:718, LYS A:808	conventional hydrogen bond,	
	LEU A:744, TRP A:718, VAL A:715	carbon hydrogen bond,	
	ALA A:748, GLN A:711, GLY A:683	unfavorable positive-positive	
	PRO A:682, VAL A:684	alkyl, pi-alkyl	

Figure 4 illustrates the interaction of compound 22 with the receptor, whereas Figure 5 shows the interaction of compound 7 with its receptor. The unique compound-receptor binding outcomes were attributed to the existence of salt bridges, pi-sulfur, and pi-sigma interactions.Figure 4 2D and 3D representations of compound 22 in the active site of the 5T8E receptor.

Figure 4

Figure 5 2D and 3D representations of compound 7 in the active site of the 5T8E receptor.

Figure 5

Drug-likeness and pharmacokinetics studies

SwissADME is an online tool designed for studying the pharmacokinetic, physicochemical, and medicinal chemistry sensitivity of small molecules.51 After the molecules are imported into SwissADME, the results are displayed in a web browser for ease of visualization, and a PDF version of the report is saved.52 The compounds under investigation were examined to determine drug-likeness. The molecular weight of these compounds was ≤500 MW, the number of hydrogen bond donors (HBD) was five or fewer, and the number of hydrogen bond acceptors (HBA) was 10.6 (Table 7). Their bioavailability score of 0.55 indicated the compounds' ability to be absorbed, and their synthetic accessibility of 3.42–3.52 predicted their ability to be conveniently synthesized in laboratory settings. Hence, our findings suggested that compounds 22 and 7 inhibitors were candidates for synthesis.Table 7 Predicted drug-likeness properties of the selected compounds.

Table 7Molecule	Molecular weight	HBA	HBD	MLogP	Synthetic accessibility	Bioavailability score	Lipinski violation	Drug-likeness	
22	470.59	3	4	2.41	3.52	0.55	0	Yes	
7	491.01	3	4	2.68	3.42	0.55	0	Yes	

Drug metabolism depends on the class of enzymes (cytochrome p450), including CPY1A2, CPY2C19, CPY2C9, CPY2D6, and CPY3A4. The investigated compounds had favorable predicted pharmacokinetic properties (Table 8). These compounds are inhibitors of CPY1A2, 2C19, 2C9, and 34A. Additionally, they are not substrates of P-gp and cannot penetrate the blood–brain barrier. Therefore, these compounds have positive pharmacokinetic properties and may serve as potential drugs for inhibiting the PC3 cancer cell line, because they passed the analysis for drug friendliness, had additional favorable physicochemical qualities, and followed Lipinski's rule of five. Consequently, the selected compounds may be candidates for preclinical trials. In addition, the bioavailability radars of molecules 22 and 7 are displayed in Figure 6.Table 8 Predicted pharmacokinetic properties of the selected compounds.

Table 8S/no	GI absorption	BBB permeant	P-gp substrate	CPY inhibitors	
CPY1A2	CPY2C19	CPY2C9	CPY2D6	CPY34A	
22	Low	No	No	Yes	Yes	Yes	No	Yes	
7	Low	No	No	Yes	Yes	No	No	Yes	

Figure 6 Bioavailability tracking system for molecules 22 and 7.

Figure 6

This study provides insight into the activity of thiosemicarbazone compounds, and offers information to support future studies on molecular modification and the in silico design of other thiosemicarbazone compounds, with the aim of improving the receptor binding affinity of ligands 22 and 7. However, further validation through in vivo and in vitro analysis is recommended, because thiosemicarbazone might be a promising plant source for a drug molecule that can treat prostate cancer.

Conclusion

This study used QSAR, molecular docking, and pharmacokinetic techniques on thiosemicarbazone-indole derivatives to generate a model. Of the five models constructed, model 1 was selected and found to be statistically fit, as evidenced by the following validation parameters: R2 = 0.972517, Radj = 0.964665, cRp2 = 0.780922, and LOF = 0.076524. The QSAR model indicated that an increase in SsCH3, and decreases in SHBd, JGI2, and RDF60p, would enhance the biological activity of the thiosemicarbazone-indole derivatives, thereby suggesting the potential of these compounds as effective remedies for treating the PC3 cancer cell line.

The best compounds, 22 and 7, were subjected to molecular docking, and demonstrated positive stability and interactions with amino acid residues at the crucial target site. The most common residues across all reported compounds were VAL, ALA, LYS, GLY, PRO, TRY, ARG, GLU, and GLN.

Pharmacokinetic and drug-likeness predictions indicated that both compounds 22 and 7 were within the maximum accepted oral bioavailability ranges for drug molecules. Moreover, the two chosen compounds adhered to Lipinski's rule of five. Overall, our findings suggest that compounds 22 and 7 may be promising potential candidates for further development as orally bioavailable drugs targeting the PC3 cancer cell line.

Source of funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of interest

The authors have no conflict of interest to declare.

Ethical approval

There are no ethical issues.

Authors contributions

AIK devised and designed the experiment, performed the experiment, analyzed and interpreted the data, and wrote the paper. AU provided directives and technical advice. MTI performed the experiment and wrote the paper. ITB and ABU provided technical assistance. All authors have critically reviewed and approved the final draft and are responsible for the content and similarity index of the manuscript.

Peer review under responsibility of Taibah University.
==== Refs
References

1 Cheesman M.J. Iianko A. Blonk B. Cock I.E. Developing new antimicrobial therapies: are synergistic combinations of plant extracts/compounds with conventional antibiotics the solution? Pharmacogn Rev 11 22 2017 57 72 10.4103/Phrev.Phrev_21_17 28989242
2 Sofowora A. Ogunbode E. Onayade A. The role and place of medicinal plants in the strategies for disease prevention Afr J Tradit Complement Altern Med 10 5 2013 210 220 10.4314/ajtcam.v10i5.2 24311829
3 Reynolds T. Wessel M. Konagurthu S. Crew M. Computational methods-formulation development: an innovative, simulation-based approach Drug Dev Deliv 2016 https://drugdev.com/computational-methods-formulation-development-an-innovative-simulation-based-approach/
4 Bruchovsky N. Rennie P.S. Batzold F.H. Goldenberg S.L. Fletcher T. McLoughlin M.J. Clin Endocrinol Metab 67 1988 806
5 Thomas L.N. Douglass R.C. Vessey J.P. Gupta R. Fontaine D. Norman W. J Urol 170 2003 2019 14532845
6 Rawla P. Epidemiology of prostate cancer World J Oncol 10 2019 63 89 31068988
7 Siegel R. Naishadham D. Jemal A. CA Cancer J Clin 63 2013 11 23335087
8 Siegel R.L. Miller K.D. Jemal A. CA Cancer J Clin 65 2015 5 25559415
9 Kaur Kamal Preet Jaitak Vikas Recent development in indole derivatives as anticancer agent for breast cancer 2019 10.2174/1871520619666190312125602
10 Babu Lagu Surendra Prasad Yajella Rajendra Bhandare Richie R. Shaik Afzal B. Design, synthesis, and antibacterial and antifungal activities of novel trifluoromethyl and trifluoromethoxy substituted chalcone derivatives Pharmaceuticals (Basel) 13 2020 375 10.3390/ph13110375 33182305
11 Omotoyi S.O. Fadipe O.I. Computational prediction of HCV RNA polymerase inhibitors from alkaloid library Lett Appl Nano Biosci 11 2021 3661 3671
12 Kumar Akhalesh Sharma Saurabh Mishra Sudhanshu Ojha Smriti Upadahyay Pawan ADME prediction, structure-activity relationship of boswellic acid scaffold for the aspect of anticancer and anti-inflammatory potency Anticancer Agents Med Chem 2023 10.2174/1871520623666230417080437
13 Alomar K. Khan M.A. Allain M. Bouet G. Synthesis, crystal structure and characterization of 3-thiophene aldehyde thiosemicarbazone and its complexes with cobalt (II), nickel (II) and copper (II) Polyhedron 28 2009 1273 1280
14 Sibuh B.Z. Gupta P.K. Taneja P. Khanna S. Surkar P. Pachisia S. Synthesis, in silico study and anti-cancer activity of thiosemicarbazone derivatives Biomedicines 9 10 2021 1 19 10.3390/biomedicines9101375
15 Tadar R. Chavda N. Shah M.K. Synthesis and characterization of some new spectroscopic characterization, biological screening and in vitro cytotoxic studies of 4-methyl-3-thiosemicarbazone-derived Schiff bases and their CO (II), Ni (II), Cu (II), and Zn (II) complexes Appl Organomet Chem 33 2019 1 23
16 Dong G. Wu Y. Sun Y. Lun N. Wu S. Zhang W. Identification of plant catalytic inhibitors of human DNA topoisomerase II by structure-based visual screening Med Chem Comm 9 11 2018 1142 1146
17 Yang F. Liang H. Designing anticancer multitarget metal thiosemicarbazone prodrug based on the nature of the binding site human serum albumin carrier Future Med Chem 10 2018 1881 1883 29925269
18 Pape V.F.S. Toth S. Furedi A. Szebenyi K. Lovrics A. Szabo P. Design, synthesis and biological evaluation of thiosemicarbazone, hydrazinobenzothiazole and arylhydrazones as anticancer agents with a potential to overcome multidrug resistance Eur J Med Chem 117 2016 335 354 27161177
19 Wang Y. Gu W. Shan Y. Liu F. Xu X. Yang Y. Design, synthesis and anticancer activity of novel nopinone-based thiosemicarbazone derivatives Bioorg Med Chem Lett 27 2017 2360 2363 28431878
20 Yee E.M.H. Brandl M.B. Black D.S. Vittorio O. Kumar N. Synthesis of isoflavene thiosemicarbazone hybrids and evaluation of their anti-tumor activity Bioorg Med Chem Lett 27 11 2017 2454 2458 28408225
21 Nishida C.R. Ortiz de Montellano P.R. Bioactivation of antituberculosis thiomide and thiourea prodrugs by bacterial and mammalian flavin mono-oxygenase Chem Biol Interact 192 2011 21 25 20863819
22 Sarkanj B. Molnar M. Cacic M. Gille L. 4-methyl-7-hydroxycoumarin antifungal and antioxidant activity enhancement by substitution with thiosemicarbazide and thiozolidinone moieties Food Chem 139 2013 488 495 23561135
23 Gupta H.K.A.M. Recent advances in thiosemicarbazone as anticancer agents Int J Pharm Chem Biol Sci 8 2018 259 265
24 Heffeter P. Pape V.F.S. Enyedy E.A. Kappler B.K. Szakacs G. Kowol C.R. Anticancer thiosemicarbazones: chemical properties, interaction with iron metabolism and resistance development Antioxid Redox Signal 30 2019 1062 1082 29334758
25 Summers K.L. A structural chemistry perspective on the antimalarial properties of thiosemicarbazone metal complexes Mini Rev Med Chem 19 2019 569 590 30324878
26 Murren J. Modiano M. Clairmont C. Lambert P. Savaray N. Doyle T. Phase I and pharmacokinetic study of triapine, a potent ribonucleic reductase inhibitor administered daily for five days in patients with advanced solid tumors Clin Cancer Res 9 2003 4093 4100
27 Karp J.E. Giles F.J. Gojo I. Morris L. Greer J. Johnson B. A phase I study of the novel ribonucleotide reduction inhibitor 3-aminopyridine-2-carboxaldehyde thiosemicarbazone (3-AP, Triapine) in combination with the nucleoside analog fludarabine for patients with refractory acute leukemias and aggressive myeloproliferative disorder Leuk Res 32 1 2008 71 77 17640728
28 Ma B. Goh B.C. Tan E.H. Lam K.C. Soo R. Leong S.S. A multicenter phase II trial of 3-aminopyridine-2-carboxaldehyde thiosemicarbazone (3-AP, -Triapine) and gemcitabine in advanced non-small-cell lung cancer with pharmacokinetics evaluation using peripheral blood mononuclear cells Invest New Drugs 26 2 2008 169 173 17851637
29 Fadeyi O.O. Adamson S.T. Myles E.L. Okoro C.O. Novel fluorinated acridone derivatives part I: synthesis and evaluation as potential anticancer agents Bioorg Med Chem Lett 18 2008 4172 4176 18541426
30 Harshita Sachdeva Jaya Mathur Anjali Guleria Indole derivative as potential anti-cancer agents: a review J Chil Chem Soc 65 3 2020 4900 4907 10.4067/s07179707220000204900
31 Kumari A. Singh R.K. Bioorg Chem 103021 2019
32 Singh S.J. Singla R. Jaitak V. Anti-cancer Agents in Medicinal Chemistry (Formerly Current Chemistry-Anti-Cancer Agents) 16 2016 160
33 Zhang M.Z. Chen Q. Yang G.F. Eur J Med Chem 89 2015 421 25462257
34 Suzen S. Curr Org Chem 21 2017 2068
35 Bal T.R. Anand B. Yogeeswari P. Sriram D. Bioorg Med Chem Lett 15 2005 4451 16115762
36 Patel T. Gaikwad R. Jain K. Ganesh R. Bobde Y. Ghosh B. ChemistrySelect 4 2019 4478
37 He Z.X. Huo J.L. Gong Y.P. An Q. Zhang X. Qiao H. Designed, synthesis and biological evaluation of novel thiosemicarbazone-indole derivatives targeting prostate cancer cells Eur J Med Chem 2020 112970 10.1016/j.ejmech.2020.112970
38 Ibrahim M.T. Uzairu A. Shallangwa G.A. Uba S. In-silico activity prediction and docking studies of some 2,9-disubstituted 8-phenylthio/phenylsulfinyl-9H-purine derivatives as anti-proliferative agents Heliyon 6 2020 e03158 10.1016/j.heliyon.2020.e03158
39 Friedman J.H. Multivariate adaptive regression splines Ann Stat 1991 1 67
40 Troyer J.R. The multiple discoveries of the first hormone herbicides Weed Sci 49 2001 290 297
41 Tropsha A. Gramatica P. Gombar V.K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSAR models Mol Inform 22 2003 69 77
42 Bakchi Bulti Krishna Ambati Dileep Sreecharan Ekambarapu Ganesh Veeramallu Bala Jaya Niharika Muraboina Maharshi Suryadevara An overview on applications of SwissADME web tool in the Design and Development of anticancer, antiturbercular and antimicrobial agents. A medicinal chemist perspective J Mol Struct 2022 132712 10.1016/j.molstruct.2022.132712
43 Lagu S.B. Yejella R.P. Nissankararao S. Bhandare R.R. Golla V.S. Subrahmanya Lokesh V.B. Antitubercular activity assessment of fluorinated chalcones, 2-aminopyridine-3-carbo-nitrile and 2-amino-4H-pyran-3-carbonitrile derivatives 2020. In vitro, molecular docking and in silico drug likeness studies PLoS One 17 6 2022 e0265068 10.1371/journal.pone.0265068
44 Abdulfatai U. Uzairu A. Uba S. Molecular docking and quantitative structure-activity relationship study of anticonvulsant activity of aminobenzole derivatives Beni-Suef Univ J Basic Appl Sci 7 2 2018 204 214
45 Silwoski G. Kothiwale S. Meiler J. Lower E.W. Jr. Computational methods in drug discovery Pharmacol Rev 66 1 2014 334 395 10.1124/pr.112.007336 24381236
46 Fukumushi Y. Yamasaki L. Takeuchi K. Kurosawa T. Nakamira H. Quantitative structure-activity relationship (QSAR) models for docking score correction Mol Inform 361 1–2 2017 1600013 10.1002/minf.201600013
47 Adeniji S.E. Uba S. Uzairu A. Authur D.E. A derived QSAR model for predicting some compounds as potent antagonist against Mycobacterium tuberculosis: a theoretical approach Adv Prev Med 2019 10.1155/2019/5173786
48 Veersamy R. Harish R. Abhishek J. Shalini S. Christapher P.V. Ram K.A. Validation of QSAR models: strategies and importance Int J Drug Des Discov 2 3 2011 511 519 10.1016/JIJDDD.2011.07.007
49 Nnyigide O.S. Lee S. Hyunk K. In-silico characterization of the binding modes of surfactants with Bovine serum albumin Sci Rep 9 2019 10643 10.1038/s41598-019-47135-2
50 Ramsey R. Popovic-Nikolic M. Nikolic K. Uliassi E. Bolognesi M. A perspective on multitarget drug discovery and design for complex disease Clin Transl Med 7 3 2018 10.1186/s40169-018-011-2
51 Daina A. Michielin O. Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness, and medicinal chemistry friendliness of small molecules Sci Rep 7 2017 42717
52 Daina Antoine Zoete Vincent Application of the SwissDrugDesign online resources in virtual screening Int J Mol Sci 20 2019 4612 10.3390/ijms20184612 31540350
