*2.2. Biological Activity*

The compounds were subjected to an initial evaluation for potential cytotoxic activity against different cancer cell lines, namely, HepG2, MCF-7, and HCT-116 cells, at 50 μM. Cell viability was measured using MTT assay. Among the screened 14 compounds, 3 (**5h**, **5i**, and **5j**) did not show any cytotoxic activity against HepG2 cells. The concentration of the active compounds that killed 50% of the cells (IC50) was evaluated against HepG2 cells. Compound **5g** (IC50 = 5.00 ± 0.66 μM) was the most potent active compound, showing more potent activity than that of the standard chemotherapeutic drug *cis*platin (IC50 = 9.00 ± 0.76 μM) (Table 1). Moderate anticancer activity against HepG2 cells was observed for compounds **5a** and **5m** (IC50 = 10.00 ± 0.47 and 17.00 ± 0.68 μM, respectively).

The same three inactive compounds (**5h**, **5i**, and **5j**) did not show activity against MCF-7 or HCT-116 cells (Table 1). The other 11 tested compounds (IC50 ≤ 9.00 μM) showed superior activity to that of cisplatin (IC50 = 9.00 ± 0.29 μM) against MCF-7 cells (Table 1); only compounds (**5c**, **5f**, **5g**, and **5l**) (IC50 < 3.00 μM) were more potent than cisplatin (IC50 = 3.00 ± 0.24 μM) against colon cancer cells (Table 1). The present study showed that compound **5g** retained broad anticancer activity against the three tested cell lines of liver, breast, and colorectal cancers; HepG2, MCF-7, and HCT-116 cells, respectively.


**Table 1.** Results of anticancer activity against HepG2, MCF-7, and HCT-116 cells.


**Table 1.** *Cont.*

a IC50 (μM) was evaluated using MTT assay and ± is the standard deviation from three independent experiments. b NA:meansthatthetestedcompounddidnotshowanticanceractivityat50μM.c NT:didnottestedagainstthe

 MCF-7 cells. #### *2.3. E*ff*ect of the Dibromo on the Anticancer Activity*

The structure-activity relationship between the previously reported spirooxindole analogues **4b**, **4c**, **4d**, **4f**, and **4i-n** [9] and the diboromo-substituted spiroxindoles **5b**, **5c**, **5d**, **5f**, and **5i-n** is described. In fact, the IC50 values of Table 1 clearly show that the replacement of the H atoms of the previously reported compounds **4b**, **4c**, **4d**, **4f**, and **4i-n** with that of its analogues with the Br produced a significant decrease in the inhibitory growth effect on the HEPG2 cell line. On the other hand, compounds **5b**, **5c**, **5f**, **5k,** and **5m** (dibromo-substituted) showed better activity against HCT-116 cells than their dibromo-unsubstituted indole counterparts. Compounds **5d**, **5l**, and **5n** showed less activity than the compounds **4d**, **4l**, and **4n,** respectively. Compounds **5i** and **5j** were not active and compounds **4i** and **4j** presented some activity (Table 1).

#### *2.4. Shape Alignment by Rapid Overlay Chemical Structure (ROCS) Analysis*

Shape and electrostatic potential are two fundamental molecular descriptors for computational drug discovery, because in protein ligand binding, the shape of a ligand has to conform in large degree to the shape of a protein binding site. The electrostatic potentials presented in the binding site have to complement the electrostatic potential of the protein. Accordingly, it is very important to model and understand protein ligand bindings correctly. The 3D shape structure exhibits good neighborhood behavior, in which high similarity in shape reflects high similarity in biology. Shape similarity can have different applications, such as virtual screening, lead-hopping, molecular alignment, pose generation, and predictions.

ROCS is a tool used in shape similarity studies. ROCS requires a query, which is an active molecule with some biological activities in at least one 3D conformation. It also requires a database of the molecules of the compounds of interest. Consistent with these standards, our compounds (database set) showed similarity to standard compound **BI-0225** (Figure 2). Compound **5g** showed high similarity to **BI-0225** in terms of its oxindole moiety and oxoindole ring.

**Figure 2.** Shape similarity of **5g** with **BI-0252** as analyzed by Rapid Overlay Shape Chemical Structure (ROCS) and visualized by VIDA application.

### *2.5. Predicted Pharmacokinetics and Pharmacodynamics Parameters*

Absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction for drug candidates is mandatory in the drug design process, as these parameters contribute to determining the failure of approximately 60% of all drugs in the development and approval phases. It is well-known that ADMET prediction is performed at the last stage of the drug development process with high cost and effort. At present, ADMET is determined at the beginning of drug discovery stages in order to eliminate molecules with poor ADMET properties from the drug discovery pipeline with the aim to save research costs. In this regard, computational tools were used to predict ADMET properties in this study [14].

The Caco-2 cell, percentage of human intestinal absorption (HIA), and skin permeability models have all been suggested as reliable in vitro models to estimate oral drug absorption and transdermal delivery [15]. Drug penetration to the blood brain barrier (BBB) provides insight into drugs that act on the central nervous system and on plasma protein binding (PPB). Compared to the other compounds, **5g** showed the lowest BBB penetration value (0.017) and a low value in the Caco-2 cell model (18.80). All compounds showed high PPB and HIA values, as well as very low skin permeability values in the range of −1.80 to −2.79 (Table 2).


**Table 2.** Predicted pharmacokinetic and pharmacodynamic parameters of the most active compounds.

HBD, hydrogen bond donor; HBA, hydrogen bond acceptor; BBB, blood brain barrier; PPB, plasma protein binding; HIA, percentage human intestinal absorption; Caco-2 value, permeability to Caco-2 (human colorectal carcinoma) cells in vitro.

#### *2.6. Ligand E*ffi*ciency (LE) and Lipophilic E*ffi*ciency (LipE)*

In the current study, for optimization assessment, LE was calculated [18]. The parameter LE has a crucial role in "lead optimization for drug-like candidate" properties [19]. Compounds with the highest activity were selected for evaluation against sensitive cancer cell lines (breast and colon cancer cells).

LE was calculated using the following equation [20]:

$$\mathrm{LE} = (\mathrm{pIC}\_{50} \times 1.37)\% \mathrm{NHA}$$

IC50 = half-maximal inhibitory concentration (in terms of molar concentration); NHA = non-hydrogen atom.

The compounds had an LE value in the range of 0.19–0.26 except for compound **5n** (Table 3). All compounds exhibited higher LE values in breast cancer cells than in colon cancer cells, especially compounds **5c**, **5e**, and **5l** (LE = 0.26), all of which were structural isomers.

The recommended LE value should be in the range of 0.3. The acceptable LE value should be higher than 0.3.

#### *2.7. Lipophilic E*ffi*ciency (LipE) or Ligand Lipophilic E*ffi*ciency (LEE)*

Lip E or LLE is an avenue to determine compound a ffinity with respect to its lipophilicity.

Nowadays, the lipophilic e fficiency (LipE) index (LEE), which includes lipophilicity and potency, is becoming more and more popular in drug design. It allows for the normalization of observed potency with changes in the lipophilicity, and it is considered an e ffective and practical tool for keeping lipophilicity under control to avoid any "molecular obesity".

LipE or LLE is calculated as the di fference between the potency and log P as illustrated in the following equation:

$$\text{Lip E} = \text{pIC50 - cLog P}$$

According to data revealed in Table 3, compound **5g** showed best value in comparison to other derivatives between both cell lines.


**Table 3.** Summary of ligand e fficiency scores for the target compounds.

**Compounds R NHA cLog P Breast Cancer Cells Colon Cancer Cells pIC50 LE LipE (LEE) pIC50 LE LipE (LEE) 5g** 41 4.47 5.39 0.22 0.92 5.55 0.19 1.08 **5l** 37 6.72 5.52 0.26 -1.2 5.55 0.2 -0.17 **5m** 37 5.56 5.34 0.2 -0.22 5.39 0.2 -0.17 **5n** 43 7.49 5.3 0.17 -2.19 5.43 0.17 -2.06

**Table 3.** *Cont.*
