## 2 Results
### Bioactivity Screening  
According to the results outlined in Section 2.1, peptides with the best acceptable biocompatibility with the usual drug administration were peptide 3 (\({}^{17}\)LVFF [20]) and peptide 4 (\({}^{16}\)KLVF [19]). In addition, regarding the chemical compounds used for conjugation, between hexanoic acid, octanoic acid, taurine, indole-3-butyric acid, 3,5-dinitrosalicylic acid and nicotinic acid, which presents an acceptable dispersion compatible with oral administration, we chose to further investigate the nicotinic acid-peptide (3-12 and 4-12). The fatty acids taken into account, hexanoic acid (2) and octanoic acid (4), have no additional therapeutic effects, while for indole-3-butyric acid (9), even this plant growth regulator poses no known risks to humans but has no neuroprotective outcome [40]. Nicotinic acid (NA), also known as niacin, regulates the cholesterol level and suppresses the mitogen-activated protein kinase pathway and attenuates brain injury after cardiac arrest in rats [41]. Hence, we further chose to investigate our designed drugs based on the sequence of the peptide 3 (\({}^{17}\)LVFF [20]) and peptide 4 (\({}^{16}\)KLVF [19]) with nicotinic acid. Using the bioactivity screening (see the methodology described in Section 2), we investigated which class of active biomolecules from the human body, such as proteins, enzymes and receptors, could interact with these proposed drugs. Our purpose was to analyze the possible influence on the therapeutic effect of our peptides. These interactions with peptides and peptide conjugates 3 (\({}^{17}\)LVFF [20]), 3-12 (NA-\({}^{17}\)LVFF [20]), 4 (\({}^{16}\)KLVF [19]) and 4-12 (NA-\({}^{16}\)KLVF [19]), listed by each class of biomolecules based on probability, are presented in Figure 3.  
According to Figure 3, there are several classes of biomolecules which could interact with \({}^{17}\)LVFF [20] and \({}^{16}\)KLVF [19], including their conjugates with NA. Figure 3a shows that the highest probability of interactions for \({}^{17}\)LVFF [20] is with protease (28%), followed by Family AG protein-coupled receptor (24%). After coupling, the probability for this peptide conjugate (NA-\({}^{17}\)LVFF [20]) drastically increases for Family AG protein-coupled receptor (52%), while the probability for protease was the same. Additionally, in the case of \({}^{16}\)KLVF [19] (Figure 3b), the value obtained for protease was 32%, whereas for NA-\({}^{16}\)KLVF [19], it was raised at 32%. Regarding Family AG protein-coupled receptor, the probability remained similar following coupling (from 32% to 36%). Therefore, these results suggest that the addition of NA increases the affinity for these classes of biomolecules. Furthermore, an improvement of probability interaction with Family AG protein-coupled receptor, as observed in the data above, could represent an important step for neuroprotective functions. It was proved that the overexpression of Family AG protein-coupled receptor inhibits oxidative toxicity in the brain [42]. Moreover, humanin, a newly identified neuroprotective factor, uses the G protein-coupled formylpeptide receptor-like-1 as a functional receptor [43]. In addition, by increasing the activity of proteases, they may catalyze the cleavage for amyloid-beta peptides (1-42) and (1-40), monomer and fibrils. In addition, we performed pharmacokinetic investigations of cytochrome P450 (CYP) in the presence of our designed peptides in order to improve the knowledge regarding biocompatibility and various interactions. Thus, predicting the probability that a molecule will cause inhibition of CYPs represents a critical step for drug development. Furthermore, such an isoenzyme superfamily possesses a crucial role in drug clearance via metabolic biotransformation. Hence, the inhibition of these isoenzymes could provide us supplementary information on the major cause of pharmacokinetics-related drug-drug interactions.