When I began to study pharmacology, I enjoyed the fact that it is a basic science in the sense that most of it boiled down to a study of how molecules interact. I’ve retained much of that optimism from decades ago. I want to share some observations that may help future pharmacologists advance the field.

A first principle is that binding does not equal activation. I sometimes think that those doing computational or medicinal chemistry either don’t know this or have forgotten it. The obvious example is the (neutral) antagonist molecules, which appear to bind equally to each receptor state and produce no response. This isn’t as clean a definition as it once was, because we now have a full range of molecules that may bind with the low affinity site and produce an inverse response compared to the agonist molecules that bind and produce a response. It doesn’t matter, the fact that a molecule binds to a receptor says nothing about the resulting response.

There are many endogenous molecules that can interfere with animal or clinical studies of drugs. This is sometimes, although not often, recognized in the handling and care of the animals. It has also been previously recognized long ago as the Law of Initial Value, which found that drugs administered to various experimental subjects and patients don’t always act in the way that was intended. This law is often associated with the Weber-Fechner law, because at the higher stimulus intensities, the response is often reduced or even reversed. For instance, under stress, a subject may show a decrease in heart function when given a positive inotrope such as epinephrine. This is extremely difficult to control in either animal or clinical studies.

Binding molecules (ligands) have structural elements that complicate computer models. These structural elements include ligands that can have more than one pharmacophore inherent within even rather small molecules. This has been known for many years, but hasn’t been adequately examined. The best and perhaps most simple example is the acetylcholine molecule. Acetylcholine was found to activate the parasympathetic nervous system. Later it was found that there were at least two separate receptors for this system labeled nicotinic and muscarinic based upon the agonists, nicotine and muscarine. Years later, the ingenious observation of a young scientist, Dr. Saul Maayani, recognized that one could observe the two sides of the acetylcholine molecule comprising the muscarinic pharmacophore versus the opposite side, which comprised the nicotinic pharmacophore. This is probably true for most molecules studied today, but little concern is devoted to the fact that there might be inherent conformations of these binding ligands that modulate and possibly even inhibit its own binding to a target receptor. For instance, we could ask, what percent of the acetylcholine molecule exists in conformations that favor (or modulate or inhibit) its binding with the nicotinic receptor. These are questions that are very much environmentally dependent and for which we don’t know reasonable physiological parameters.

Electrostatic charges are important, but only within the last ten years have they been incorporated into the computer modeling of drug-receptor interactions. The changes in electrostatic charges would most likely far overwhelm any conformational twists or turns that have been developed into complete scenarios for drug activation of the G protein-coupled receptors (GPCRs). Getting charges right is very difficult. There’s the surrounding charges, fields generated by the membrane, solvent, counter ions and charged membrane lipids and other molecules that interact with the G protein-coupled receptors. So far, our computers or imaginations aren’t up to the task. This doesn’t mean that we must give up. It means that we should be smarter about how we model these systems. For instance, instead of making the interaction of a ligand or drug with a receptor more complicated by piling on more and more secondary and tertiary molecules, let’s just observe what the very initial reaction would look like. I believe that a great deal can be learned from this approach. One reason for my optimism is that enzymes also interact with molecules, but they don’t sit within membranes like the GPCRs. Enzymes often have cofactors, but don’t include a plethora of additional molecules to display complex kinetics. Enzymes show most of the subtleties shown by GPCRs and in fact much of pharmaceutical theory and development has borrowed heavily from the field of enzymology. Enzymology has its own problems as outlined by William Lipscomb’s work with the enzyme, Carboxypeptidase A. Among these problems is that the same ligand could produce different enzyme kinetics while presumably binding at the same site of the enzyme. These and other difficulties, such as substrate inhibition, are often glossed over when doing experiments. The fact is that many enzymes having similar kinetics to drug-receptor interactions exhibit similar problems that need further examination.

Enzyme and receptor studies have several areas in common, but some of the protocols in enzymology aren’t properly carried over to pharmacology. For instance, the pH-dependence of response, which most competent enzymologists know should be examined to not only show the pH profile of the enzyme but to gain insights into the possible mechanism, should be required. In pharmacology, this is rarely done for studies of receptors. Investigators certainly don’t report such studies if they’re doing them. This is also more complicated than it looks because there are many factors that could alter the pH-dependence of receptors, including the ionic strength, type and strength of the buffer, temperature and atmosphere (such as in cell cultures). Many of these factors are not reported in the experimental literature, yet they are crucial to truly understanding the experimental system.

The reduction-oxidation potential is another neglected area. Pharmacologists routinely take tissues from an animal and mount them in some buffered bath at ambient atmospheric temperature and pressure. One problem with this is that in vivo, these tissues exist under different conditions. One difference is that the partial pressure of oxygen is much higher under the conditions of the isolated bath; whereas, it may be as low as 5-15 mmHg in vivo. This may cause increased oxidation of sensitive elements of many proteins including GPCRs and enzymes -not to mention the lipids (this is also important for cell culture studies). Proteins with cysteines or free thiol groups are particularly susceptible to oxidation under these experimental conditions. It is very difficult to control or prevent these oxidation conditions and most experiments don’t mention or account for these potentially confounding effects.

After more than two decades of study, I’ve found that only two states are all that are needed to adequately describe most drug-receptor interactions. The fact that enzymology and pharmacology use the fractional or proportional response to calculate the response of enzyme and receptor systems is problematic. I realize that this goes against established biochemical and pharmacological theory, but the fact is that the “net shift” represents a response that is a better descriptor. This is primarily because the “net shift” is based upon Le Chatelier’s principle, which describes the response of chemical systems to perturbations. The unequal binding of ligands to either an enzyme or receptor in two states will perturb that enzyme or receptor. This has never been calculated before, because it requires a bit of a trick. The trick involves seeing that there are essentially two ways to produce a perturbation on a physical balance (this also obeys an important physiological law, Weber’s law as known as the Weber-Fechner law).

The conclusions are that molecular modeling is very difficult if not impossible and that pharmacological theory needs to be revamped. Other than those I can’t think of much else, except that experimentalists are also doing impossible jobs and need to rethink their past and future work. I wish you all the best as we trudge forward to make science and pharmacology a shining example of how thinking scientists can create marvelous science.