The objectives of drug design

I'll open the post on drug design objectives with photos from a most enjoyable and informative visit to the Australian Synchrotron early in 2010 when I was helping with fragment library design at CSIRO.

I’ve been meaning for ages to do a post like this and was finally goaded into action when I recently looked at two short videos from interviews with Sir Demis Hassabis, founder of Google DeepMind and Isomorphic Labs, and one of the 2024 Nobel Chemistry Prize laureates. Predicting the 3D structure of a protein from its amino acid sequence is a capability that has been eagerly sought for a long time and, as we celebrate the award, we need to also recognize the remarkable foresight of those who launched the Protein Data Bank in 1971 with just seven X-ray crystal structures. We also need to recognize that protein structures are inherently flexible and subject to post translational modification such as glycosylation and phosphorylation. Furthermore, the crystal structure that has actually been determined might correspond to a relatively small portion (for example, a tyrosine kinase domain) of a much larger structure such as a dimeric growth factor receptor.

Let’s take a look at the two videos. In the first video, Sir Demis suggests that the end of disease is “within reach maybe in the next decade or so” and it’s worth pointing out that most of the cost of bringing a drug to market comes from clinical development rather than the actual discovery of the drug (nobody spends “ten years and billions of dollars to design just one drug” and it would be more accurate to say that we do so to see if what we've designed really is a drug). Furthermore, work in the late stage of drug discovery when project teams are assessing their best compounds should not really be regarded as drug design. In the second video, Sir Demis acknowledges that “knowing the structure of a protein is only one step in the drug discovery process” although it’s not clear exactly how “many adjacent AlphaFolds” are going to meaningfully address the issues of side effects.

Drug design is frequently asserted to be a multi-objective exercise and, in this post, I’ll be trying to discuss this in a way that I hope will be helpful to drug discovery scientists using artificial intelligence (AI) and machine learning (ML) in design. The ultimate aim of drug design is to identify compounds (and biological entities such as therapeutic antibodies) that can be used to treat diseases without harming patients and I suggest that this can be stated as three design objectives. My view is that the term 'multi-objective' is more appropriate than 'multi-parameter' in the context of drug design because even against a single objective design can involve optimization of multiple parameters. One characteristic of drug design is that the design process is over long before we get to find out how successfully the outputs of design perform their function (in design of materials it's possible to evaluate design outputs more directly). I recall a Head of Research and Development at Zeneca describing the process as "like steering an oil tanker".

I prefer to use the more general term ‘bioactivity’ to describe the effects of drugs on targets (and anti-targets) because in some cases these effects cannot be meaningfully described by a single parameter such as an IC₅₀ value. As an aside this is a good point at which to celebrate the recent FDA approval of the PROTAC Vepdegestrant for treatment of ESR1m, ER+/HER2- advanced breast cancer and I'll direct readers to this most excellent and timely review on targeted protein degradation. The concentration of a drug in contact with a target (or anti-target), which varies with time, is determined by dose, and by the drug’s absorption, distribution, metabolism, and excretion (commonly referred to as ADME).  While the therapeutic and adverse effects of drugs are what the drug does to the body ADME is what the body does to the drug. Put another way, minimization of toxicity and optimizing ADME are entirely different objectives and I generally recommend that the acronym ADMET not be used.

Uncertainty is omnipresent in drug discovery and, despite what many appear to believe, AI/ML is not going to make this uncertainty vanish as if by magic. Derek was emphasizing the challenges presented by the complexity of biology long before AI came to be seen by some as a panacea for the ills of Pharma/Biotech (here’s a post from almost two decades ago and I also recommend reading his 2025 post on the “End of Disease” interview which also links relevant previous posts). The complexity of biology means that even if we knew the extent of target engagement in vivo (which varies with both dose and time) we wouldn’t generally be able to predict the in vivo effects of the drug with any confidence in the absence of other information. There is also uncertainty in exposure to consider and the concentration of a drug at its site(s) of action generally cannot be measured in vivo unless the target(s) are in direct contact with plasma. Uncertainty in exposure for intracellular targets is also a clinical development issue because failure in a Phase II trial may simply reflect inadequate exposure (we noted in KM2013 that “one can argue that a typical Phase I trial provides an incomplete description of distribution”). I recommend that everybody working in drug discovery and chemical biology read Smith & Rowland (2019) Intracellular and Intraorgan Concentrations of Small Molecule Drugs: Theory, Uncertainties in Infectious Diseases and Oncology, and Promise DMD 47:667-672 DOI. I argue in NoLE that achieving controllability of exposure should be seen as an objective of drug design.

One way that pharmacokinetic/pharmacodynamic (PK/PD) modellers address the issue of intracellular exposure is to assume that the concentration of drug in contact with its target(s) (and anti-targets) equals its unbound concentration in plasma (which can be measured in real time) and this assumption is referred to as the ‘free drug hypothesis’ (‘principle’ and ‘theory’ are also used in this context although I personally prefer ‘hypothesis’ because it’s an assumption we’re making). There are two scenarios under which the approximation of the concentration of drug at its site(s) of action by its unbound concentration in plasma is known to be unreliable. The first scenario is that there is significant active transport at one or more points on the path between plasma and the drug’s site(s) of action (active efflux is a common problem, especially in CNS drug discovery, although active influx will still cause the assumption to break down). The second scenario is that the pH at the drug’s site(s) of action differs from plasma pH (as would be the case for a lysosomal target) and that there is an ionizable group such as a basic nitrogen in the chemical structure of the drug.

While drug design does indeed have multiple objectives it really shouldn’t need to be said that if the required level of bioactivity cannot be achieved then it becomes irrelevant whether the other objectives are achieved and I’ll direct readers to M2026 (The Affinity Advantage). I see M2026 as providing a much-needed cold shower for a 2024 JMC Editorial (Property-Based Drug Design Merits a Nobel Prize; see blog post) in which it is asserted that “a discovery compound is more likely to become a drug when Fsp3 > 0.40” and that “a compound is more likely to have good developability when PFI < 7”. Nevertheless, I don’t consider M2026 to be especially useful from the perspective of defining drug design objectives because bioactivity is typically quantified by potency rather than affinity in drug discovery projects (an assay for kinase inhibition might have been run at high ATP concentration to mimic the intracellular environment) and some bioactivity objectives are defined in terms of measurements made in cell-based assays. Furthermore, bioactivity for ‘new modalities’ such as irreversible covalent inhibition and targeted protein degradation cannot be adequately described by a single parameter such as an IC₅₀ value.

I criticized the term ‘avoid-ome’ in a previous post and, with apologies for the dreadful pun, I would recommend that its use be avoided (at the risk of repetition ADME and toxicity are entirely separate issues that must be addressed separately). Furthermore, I would question whether drug designers actually need yet another ‘ome’ word and I consider the notion that embracing the avoid-ome will transform drug discovery to be fanciful. While inhibition of cytochrome P450 (CYP) enzymes is generally undesirable from a toxicity perspective a compound that was not cleared by these metabolic enzymes would greatly worry those responsible for drug safety (bear in mind why we worry about inhibition of CYPs in the first place). Furthermore, I would challenge the inclusion by M2026 of serum albumin in a list of anti-targets such as hERG (I’m not aware of anybody suffering cardiac arrest on account of their medication binding to serum albumin) and the excellent B2025 study notes that "most drugs are >95% plasma protein bound (58%), with a large fraction >99% bound (29%)". Binding to plasma proteins should actually be considered within the framework of distribution (it can be instructive to pose the question as to whether you could tell where a drug was simply from knowing the total quantity of it in the body and its unbound plasma concentration). It’s also worth mentioning that binding to plasma proteins will protect an orally-dosed drug from the metabolizing enzymes during its first pass through the liver (before it gets a chance to distribute into the tissues). Variation of the plasma concentration during the dosing interval for an orally-dosed drug is a necessary evil resulting from oral dosing and in many situations the ‘ideal’ pharmacokinetic profile would actually be that resulting from intravenous infusion (plasma concentration of the drug is maintained at a level required for therapeutically useful effects).

At this point I’ll attempt to articulate three general objectives of drug design (the only thing that I’m entirely confident about is here that I won’t get these exactly right). One of the great challenges that drug designers face is that it is usually difficult to identify compounds that simultaneously achieve all the design objectives. Specifying criteria for objectives too permissively increases the risk of choking in clinical development.  However, overly stringent specification of criteria for objectives decreases the likelihood of achieving all of the objectives and will slow the discovery process. I state these objectives in terms of ‘bioactivity’ rather than ‘potency’ to accommodate ‘new’ modalities such as irreversible covalent inhibition and targeted protein degradation although, in many cases, it will be possible to quantify the bioactivity for a compound by a single IC₅₀ or EC₅₀ value. I use ‘maximize’ and ‘minimize’ (as opposed to ‘optimize’) to frame the objectives because there is generally no penalty for identifying better compounds than you think you need. Assessing how well objectives have been achieved involves running a diverse range of assays and, as noted in this blog post on the A2025 study, it is important to be fully aware of the quantitation limits for each and every assay that you use.

I'll conclude the post with what I would argue are the three objectives of drug design:

1. Maximize on-target bioactivity.  This is the least difficult objective to specify because bioactivity characterized in the in vitro assays is likely to translate to target engagement in vivo provided that the compound can be presented to the target(s) at the required concentration. Design outputs are usually evaluated in animal models for the human disease before initiating studies in humans but the design itself is almost invariably done against in vitro end points. 
2. Minimize off-target bioactivity. It is generally more difficult to specify objectives for off-target bioactivity than for on-target bioactivity on account of the numbers and diversity of the assays involved. Design outputs are always evaluated for toxicity in animals before initiating studies in humans (as mandated by regulatory authorities) but the design itself is almost invariably done against in vitro end points.    
3. Maximize controllability of exposure. This objective, which might also be stated as 'Optimize ADME', is the most difficult of the three objectives to specify because, as noted earlier in this post, exposure generally can’t be measured for targets that are not in direct contact with plasma. At absolute minimum it is necessary to demonstrate that a pharmacokinetic profile can be achieved in animals that will maintain the (unbound) concentration of the compound at levels that we believe will result in beneficial therapeutic effects in humans. For targets not in contact with plasma the PK/PD modellers also need to be able to confidently invoke the free drug hypothesis (this is why I prefer to frame the objective in terms of exposure rather than ADME) and this requires that design outputs have good passive permeability and are not subject to active transport. In some cases it will also be necessary to demonstrate access to specific organs such as the CNS.  

 

Enthalpy-driven pharmacokinetics

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Drug design is a multi-objective endeavor. Some objectives such as maximization of affinity against target(s) and minimization of affinity against anti-targets are easily defined. Other objectives such as controllability of exposure are much less easily defined and this means that drug design is indirect. Controllability of exposure is the focus of pharmacokinetic optimization and I recently became aware of an exciting new development that will surely reshape the pharmacokinetic field and transform drug discovery beyond all recognition.

Target engagement potential and the multiple objectives of drug design 

The exciting results from another seminal study by the Budapest Enthalpomics Group (BEG) look set to revolutionize the way that we think about pharmacokinetics. The work, funded by Mothers Against Molecular Obesity (MAMO), was described in a book chapter that was prematurely posted online although the error does appear to have been recognized because the article is no longer publicly visible. In a study that will undoubtedly disrupt drug discovery, it is clearly shown that the thermodynamic signature for the binding of a ligand to a protein is predictive of the physicochemical behavior of the ligand even when that protein is absent from the system.

The theoretical treatment introduced in this groundbreaking study is formidable and the starting point is is an eigenvalue decomposition of the entropic field tensor in reciprocal heavy atom space. Machine learning using the Blofeld Optimized Ligand Lipophilicity Of Cryogenic Krypton Solvates algorithm demonstrates unequivocally that the Grayling annihilation operator can be used to eliminate the entropy (and its efficacy-limiting dependence on the definition of the standard state) from any in vivo system. This leads to highly-efficient, enthalpy-driven pharmacokinetics in which the clearance (shown to be strongly correlated with the trace of the entropic field tensor) can be significantly attenuated. "The key to successful pharmacokinetic optimization is to eliminate the elimination", explains institute director Prof Kígyó Olaj, "and we have shown, for the first time, that entropy can be exorcised from the equations of pharmacokinetics with even greater efficiency than if it had been done by Torquemada himself."

The resurrection of Maxwell's Demon

Sometimes when reading the residence time literature, I get the impression that the off-raters have re-animated Maxwell's Demon. It seems as if a nano-doorman stands guard at at the entrance of the binding site, only opening his nano-door to ligand molecules that want to get in. Microscopic Reversibility? Stop being so negative! With Big Data, Artificial Intelligence, Machine Learning (formerly known as QSAR) and Ligand Efficiency Metrics we can beat Microscopic Reversibility and consign The Second Law to the Dustbin Of History!

There were a number of things that triggered this blog post. First, I saw a recent article that got me thinking about philatelic drug discovery.  Second, some of the off-raters will be getting together in Berlin next week and I wanted to share some musings because I won't be there in person. Third, my former colleague Rutger Folmer has published a useful (dare I say, brave) critique of the residence time concept that is bang on target. 

I'm not actually going to say much about Rutger's article except to suggest that you read it. That's because I really want to examine the article on philatelic drug discovery in a more detail (it's actually about thermodynamic and kinetic profiling but I thought the reference to philately would better grab your attention). My standard opening move when playing chess with an off-rater is to assert that slow binding is equivalent to slow distribution. In what situations would you design a drug to distribute slowly?

Chemical kinetics is all about energy barriers and, the higher the barrier, the slower things will happen. Microscopic reversibility tells us that a barrier to association is a barrier to dissociation and that the ligand will return to solution along the same path that it took to its binding site. Microscopic reversibility tells you that if you got into the parking spot you can get out of it as well although that may not be the experience of every driver. The reason that microscopic reversibility doesn't always seem to apply to parking is that most humans, with the possible exception of tank drivers in the Italian army, are more comfortable in forward gear than in reverse. Molecules, in contrast, have no more concept of forward and reverse than they do of standard states, IUPAC or the opinions the 'experts' who might quantitatively estimate their drug-likeness while judging their beauty. Molecules don't actually do concepts. Put more uncouthly, molecules just don't give a toss.

I've created a graphic to illustrate to show how things might look in vivo when there is a barrier to association (and, therefore, to dissociation). We can think of the ligand molecule having to get over the barrier in order to get to its binding site and we call the top of the barrier the 'transition state'. This is a simplified version of reality (it is actually the system that passes from the unbound state through the transition state to the bound state and for some ligand-protein association there is no barrier) but it'll serve for what I'd like to say. The graphic consists of three panels and the first (A) of these illustrates the situation soon after dosing when the concentration of ligand (L) is relatively high and the target protein (P) has not had sufficient time to respond. If the barrier is sufficiently high, the system can't get to equilibrium before the ligand concentration starts to fall in what a pharmacokineticist might refer to as the elimination phase. Under this scenario the system will be at equilibrium briefly as the ligand concentration falls and I've shown this in panel B. After the equilibrium point is reached, the rate of dissociation exceeds the rate of association and this is shown in panel C. 

There's something else that I'd like you to take a look at in the graphic and that's the free energy (G) of the unbound state (P + L).  See how it goes down relative to the free energy of the bound state (P.L) as the concentration of ligand decreases. When thinking about energetics of these systems, it actually makes a lot of sense to use the unbound state as the reference but you do need to use a reference concentration (e.g. 1 M) to to do this.

When we do molecular design we often think in terms of manipulating energy differences. For example, we try to increase affinity by stabilizing the bound state relative to the unbound state. Once you start trying to manipulate off-rates, you soon realize that you can't change one thing at a time (unless you draft Maxwell's Demon into your project team).  I've created a second graphic which looks similar to the first graphic although there are important differences between the two graphics. In particular, I'm referencing energy to the unbound state (P + L) which means that the ligand concentration is constant in all three panels. Let's consider the central panel as the starting point for design. We can go left from that starting point and stabilize the bound state which is equivalent to optimizing affinity.  Stabilizing the bound state will also result in slower dissociation provided that the transition stare energy remains unchanged. This is a good thing but it's difficult to show that the benefits come from the slower dissociation and not from the increased affinity. If you raise the barrier (i.e. increase the energy of the transition state) to reduce the off-rate you'll find that you have slowed the on-rate to an equal extent.        

Before moving on, it may be useful to sum up where we've got to so far. First, ask yourself why you think off-rates will be relevant in situations where concentration changes on a longer time scale than binding. Second, you'll need to enlist the help of Maxwell's Demon if you want to reduce off-rate without affecting on-rate and/or affinity. Third, if you want to consider binding kinetics in design then it'd be best to use barrier height (referenced to unbound state) and affinity as your design parameters.

Now I'd take a look at the philatelic drug discovery article. This is a harsh term but it does capture a tendency in some drug discovery programs to measure things for the sake of it (or at least to keep the grinning Lean Six Sigma 'belts' grinning).  Some of this is a result of using techniques such as isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR) that yield information in addition to affinity (that is of primary interest) at no extra cost. I really don't want to come across as a Luddite and I must stress that measurements of enthalpy, entropy, on-rate and off-rate are of considerable scientific interest and are also valuable for improving physical models. Furthermore, I am continually awed by the exquisite sensitivity of modern ITC and SPR instruments and would always want the option to be able to measure affinity using at least one of these techniques. However, problems start when the access to enthalpy, entropy, off-rates and on-rates becomes exploited for 'metrication' and drug discovery scientists seek 'enthalpy-driven' binding simply because the binding will be more 'enthalpy-driven'. It is easier to make the case for relevance of binding kinetics although, as Rutger points out, reducing the off-rate may very well make things worse if the on-rate is also reduced. It is much more difficult to assemble a coherent case for the relevance of thermodynamic signatures in drug discovery. Perhaps, some day, a seminal paper from the Budapest Enthalpomics Group (BEG) will reveal that isothermal systems like live humans can indeed sense the enthalpy and entropy changes associated with drugs binding to their targets although I will not be holding my breath.

Unsurprisingly, the thermodynamic and kinetic profiling (aka philatelic drug discovery) article advocates thermodyanamic profiling of bioactive compounds in lead optimization projects. I'm going to focus on the kinetic profiling and it is worrying that the authors don't seem to be aware that on-rates and off-rates have to be seen in a pharmacokinetic context in order to make the connection with drug discovery. The authors may find it instructive to think about how inhibitor concentration would have varied over the course of a typical experiment in their cell-based assays. They are also likely to find Rutger's article to be educational and I recommend that they familiarize themselves with its content.

The following statement suggests that it may be beneficial for the authors to also familiarize themselves with the rudiments of chemical kinetics:

"Association and dissociation rate constants (k_on and k_off) of compound binding to a biological target are not intrinsically related to one another, although they are connected by dissociation equilibrium constant K_D (K_D = k_off/k_on)."

The processes of association and dissociation are actually connected by virtue of taking place along the same path and by having to pass through the same transition states. The difference in barrier heights for association and dissociation is given by the binding free energy. 

Some analysis of relationships between potency in a cell-based assay and  K_D, k_off and k_on were presented in Figure 6 of the article. I have a number of gripes with the analysis. First, it would be better to use logarithms of quantities like K_D, IC₅₀, k_off and k_on when performing analysis of this nature. In part, this because we typically look for linear free energy relationships in these situations. There is another strong rationale for using logarithms because analysis of correlations between continuous variables works best when the uncertainties in data values are as constant as possible. My second gripe is that the authors have chosen to bin their data for analysis and this is a great way to shoot yourself in the foot. When you bin continuous data you both reduce your data analysis options and leave people wondering whether the binning has been done to hide the weakness of the trends in the data.   I have droned at length about why it is naughty to bin continuous data so I'll leave it at that.

It's been a long post and it's time to wrap things up. If you've found the post to be 'cansativo' (sounds so much more soothing in Portguese) then spare a thought for the person who had to write it. To conclude, I'll leave you with a quote that I've taken from the abstract for Rutger's article:
  
"Moreover, fast association is typically more desirable than slow, and advantages of long residence time, notably a potential disconnect between pharmacodynamics (PD) and pharmacokinetics (PK), would be partially or completely offset by slow on-rate."